Method for adjusting working state of deploy and control ball

Abstract

The invention discloses a method for adjusting the working state of a deploy and control ball. The method comprises the following steps: positioning a deployment and control ball according to a use scene and a monitoring object of the deployment and control ball, and carrying out networking coding operation on the deployment and control ball according to initial positioning data to generate a target deployment and control ball; performing feature extraction on the video stream collected by the target monitoring ball to obtain primary video stream feature information and secondary video stream feature information; carrying out feature fusion on the feature information of the primary and secondary video streams; performing feature detection on the video stream feature fusion information, and generating an object movement track according to tracking identification data corresponding to object coordinates corresponding to a detection result; and adjusting the working state of the target deploy and control ball according to the object motion trail and a preset deploy and control rule. According to the method and the device, the technical problem that target identification is inaccurate due to the fact that the deploy and control ball can only identify and track the target from a simple monitoring environment and is easily influenced by external environmental factors in a complex environment in the related art is solved.

Description

Technical Field

[0001] The present invention relates to the field of control ball adjustment, and in particular to a method for adjusting the working state of a control ball. Background Art

[0002] The control ball is a portable intelligent equipment designed for outdoor wireless networks and power-free environments that require on-site video evidence collection and remote real-time video monitoring. The control ball adopts a stable Linux embedded operating system and uses the current advanced AI algorithm technology, 5G wireless communication technology, AGPS assisted positioning technology, and H.265 video compression technology. The control ball integrates a high-speed graphics processor, a starlight-level high-definition camera, a 5G wireless module, a Beidou GPS positioning module, a WiFi module, a Bluetooth module, and a high-capacity battery pack. The integrated intelligent PTZ camera supports 1-way 1080P high-definition video recording and high-definition image upload. Currently, 4K high-definition 8-megapixel high-definition video 5G wireless upload can be achieved. The back-end command center can realize real-time, high-definition, smooth, and stable video monitoring, two-way voice intercom, positioning, 360-degree rotating PTZ control, lens zooming and zooming, and other remote operation controls through the platform software, which facilitates the command center to conduct video analysis and emergency response to the front-line situation. The front end only needs one control ball to replace manual on-site law enforcement evidence recording, emergency control, remote control, remote real-time monitoring, etc. Powerful functions, AI intelligent recognition control ball is based on the control ball to further integrate license plate recognition, face recognition, helmet recognition, work clothes recognition, regional intrusion and other video image analysis algorithms, through computer vision technology to conduct in-depth learning of images, faces, scenes, behaviors, etc., to identify and mark images, scenes, video content, and to identify and warn customized behaviors and intentions to realize artificial intelligence, the control ball is portable and three-proof suitcase-style design and equipped with a tripod stand. When performing tasks, the control ball can be quickly placed on the tripod suction cup by magnetic suction, or directly adsorbed on the area that needs to be monitored. The control ball can be easily removed and stored in a protective box at the end of the mission. It is quick to assemble and easy to store, and can be carried and moved flexibly and conveniently. It can meet the special requirements of emergency control and rapid construction. It is suitable for construction risk management, emergency communications, dynamic service, field law enforcement, investigation and control, security and protection, emergency command, emergency command, emergency repair, inspection and line management and other industry applications, such as electric power, railways, emergency, border defense, armed police, public security, traffic police, fire protection, road administration, urban management, transportation, courts, prisons, forestry, water conservancy, environmental protection and other units.

[0003] Compared with the traditional vehicle-mounted ball, a base is extended at the bottom. The base integrates a 4G / 5G DVR / NVR encoding image transmission board and a large-capacity lithium battery to form an integrated product. It does not require installation and does not rely on external network cables and power cables. It can be quickly deployed and removed anytime and anywhere.

[0004] However, the traditional surveillance ball has the following disadvantages: the traditional surveillance ball only identifies and tracks the target from a simple monitoring environment, and is easily affected by external environmental factors in a complex environment, resulting in inaccurate target identification, and has low practicality.

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0006] An embodiment of the present invention provides a method for adjusting the working state of a control ball, so as to at least solve the technical problem in the related art that the control ball can only identify and track the target in a simple monitoring environment, but is easily affected by external environmental factors in a complex environment, resulting in inaccurate target identification.

[0007] According to one aspect of an embodiment of the present invention, a method for adjusting the working state of a control ball is provided, comprising: obtaining a usage scenario and a monitoring object of the control ball, wherein the usage scenario is an application scenario of the control ball, and the monitoring object is a monitoring target of the control ball; locating the position of the control ball according to the usage scenario and the monitoring object to obtain initial positioning data of the control ball; performing a network encoding operation on the control ball according to the initial positioning data to generate a target control ball; performing a primary feature extraction on a video stream collected by the target control ball to obtain primary video stream feature information, and performing a secondary feature extraction on the video stream to obtain secondary video stream feature information; performing feature fusion on the primary video stream feature information and the secondary video stream feature information to obtain video stream feature fusion information; performing feature detection on the video stream feature fusion information to generate object coordinates of a preset tracking object according to the feature detection result; determining tracking identification data according to the object coordinates, and generating an object motion trajectory according to the tracking identification data; adjusting the working state of the target control ball according to the object motion trajectory and preset control rules.

[0008] Furthermore, the control ball is positioned according to the usage scenario and the monitored object to obtain initial positioning data of the control ball, including: determining the initial placement position of the control ball in the target construction area according to the usage scenario; determining whether the control ball is adjusted at the initial placement position according to the dynamic and static information of the monitored object to obtain determination information; when it is determined according to the determination information that the initial placement position needs to be adjusted, the initial placement position is adjusted according to the dynamic and static information of the monitored object to locate the control ball.

[0009] Furthermore, a feature extraction is performed on the video stream collected by the target control ball to obtain video stream feature information, including: performing video collection on the monitored object through the image acquisition device built into the control ball to obtain the video stream; performing image processing on the image frames in the video stream to extract key points of the image frames; determining the similarity between the key points, and determining the geometric transformation relationship of the monitored object based on the similarity; and obtaining the video stream feature information according to the geometric transformation relationship.

[0010] Furthermore, secondary feature extraction is performed on the video stream to obtain secondary video stream feature information, including: performing image preprocessing on each frame of the video stream to obtain multiple target preprocessed images corresponding to the target control ball; performing feature extraction on the multiple target preprocessed images respectively to obtain object features of the monitored object; and generating the secondary video stream feature information based on all the object features.

[0011] Furthermore, image preprocessing is performed on each frame image in the video stream to obtain multiple target preprocessed images corresponding to the target control ball, including: filtering each frame image through a filtering algorithm to obtain an initial preprocessed image; adjusting the brightness of the initial preprocessed image to obtain the initial preprocessed image with adjusted brightness; denoising the initial preprocessed image with adjusted brightness to generate an intermediate preprocessed image; and performing image enhancement processing on the intermediate preprocessed image to generate the target preprocessed image.

[0012] Furthermore, feature detection is performed on the video stream feature fusion information to generate object coordinates of a preset tracking object based on the feature detection results, including: using a feature algorithm to perform feature extraction on each feature image corresponding to the video stream feature fusion information to obtain multiple image features; using an integral image to calculate the feature values ​​of the multiple image features to obtain multiple initial feature values; selecting the initial feature values ​​that meet the preset selection criteria to generate multiple target feature values; and generating the object coordinates based on the multiple target feature values.

[0013] Furthermore, determining the tracking and identification data according to the object coordinates includes: adjusting the working state of the target control ball according to the object motion trajectory and preset control rules to obtain the tracking and identification data.

[0014] According to another aspect of an embodiment of the present invention, a device for adjusting the working state of a control ball is further provided, the device comprising: an acquisition unit, used to acquire a usage scenario and a monitoring object of the control ball, wherein the usage scenario is an application scenario of the control ball, and the monitoring object is a monitoring target of the control ball; a positioning unit, used to locate the position of the control ball according to the usage scenario and the monitoring object, and obtain initial positioning data of the control ball; a generation unit, used to perform a network encoding operation on the control ball according to the initial positioning data, and generate a target control ball; an extraction unit, used to perform a feature extraction on the video stream collected by the target control ball A primary video stream feature information is obtained, and secondary feature extraction is performed on the video stream to obtain secondary video stream feature information; a fusion unit is used to perform feature fusion on the primary video stream feature information and the secondary video stream feature information to obtain video stream feature fusion information; a detection unit is used to perform feature detection on the video stream feature fusion information to generate object coordinates of a preset tracking object according to the feature detection result; a trajectory generation unit is used to determine tracking identification data according to the object coordinates, and generate an object motion trajectory according to the tracking identification data; an adjustment unit is used to adjust the working state of the target control ball according to the object motion trajectory and a preset control rule.

[0015] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the methods for adjusting the working state of the control ball.

[0016] According to another aspect of the present application, a processor is provided, wherein the processor is used to run a program, wherein when the program is run, any one of the methods for adjusting the working state of a control ball is executed.

[0017] In the embodiment of the present invention, the control ball is first located and network coding is performed according to the initial positioning data, then the collected video stream is feature extracted and feature fusion information is generated, then the feature fusion information is detected using a preset algorithm to generate the object coordinates of the preset tracking object in each feature image, and then data association is performed to generate the object motion trajectory, and finally the working state of the target control ball is adjusted based on the object motion trajectory and the preset control rules, so as to realize linkage processing between control balls, improve the degree of intelligence and the accuracy of tracking results, so that the control ball can accurately identify and track the target object in a complex monitoring environment, and has high practicality. This solves the technical problem that the control ball in the related art can only identify and track the target in a simple monitoring environment, but is easily affected by external environmental factors in a complex environment, resulting in inaccurate target identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 It is a hardware structure block diagram of a mobile terminal of a method for adjusting the working state of a control ball according to an embodiment of the present invention;

[0020] Figure 2 is a schematic diagram of a method for adjusting the working state of a control ball according to an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of a first feature extraction according to an embodiment of the present invention;

[0022] Figure 4 A schematic diagram of a working state adjustment device of a control ball provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] As introduced in the background technology, the control ball in the related technology only identifies and tracks the target from a simple monitoring environment. In a complex environment, it is easily affected by external environmental factors, resulting in inaccurate target identification, and has low practicality. In an embodiment of the present invention, a method and device for adjusting the working state of the control ball, a computer-readable storage medium, and a processor are provided.

[0026] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0027] The method embodiments provided in the embodiments of the present invention can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for adjusting the working state of a control ball according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0028] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the control method of the mobile robot in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific example of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] According to an embodiment of the present invention, an embodiment of a method for adjusting the working state of a control ball is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] Figure 2 is a schematic diagram of a method for adjusting the working state of a control ball according to an embodiment of the present invention. Figure 2 As shown, the method comprises the following steps:

[0031] Step S201, obtaining the usage scenario and monitoring object of the control ball, wherein the usage scenario is the application scenario of the control ball, and the monitoring object is the monitoring target of the control ball;

[0032] Step S202, positioning the control ball according to the usage scenario and the monitored object to obtain initial positioning data of the control ball;

[0033] Specifically, by using scenarios, it is determined that the control ball needs to be placed in the best position to cover the entire construction area in order to monitor the construction progress and safety situation in real time. The position is adjusted according to the monitoring target. If the monitoring target is a moving target, such as personnel and equipment on the construction site, then the control ball needs to flexibly adjust the angle and position to maintain continuous monitoring of the target.

[0034] Through the above steps, the combination of application scenarios and monitoring objects can be realized. The control ball can provide diversified monitoring solutions for different fields, thereby improving the efficiency and accuracy of the Internet of Things and intelligent monitoring.

[0035] Step S203, performing a network encoding operation on the control ball according to the initial positioning data to generate a target control ball;

[0036] Specifically, the initial positioning data, including the coordinates, number, status and other information of the control ball, is obtained and sorted. The initial data can be formatted and standardized to ensure the correctness and consistency of the data. A suitable encoding algorithm (such as hash encoding, bit encoding, etc.) is selected to encode the control ball. According to the selected algorithm, the processed data is encoded to generate a unique target control ball identification code. The encoded target control ball information can also be uploaded to the server or database for subsequent real-time monitoring and management.

[0037] Step S204, performing a primary feature extraction on the video stream collected by the target control ball to obtain primary video stream feature information, and performing a secondary feature extraction on the video stream to obtain secondary video stream feature information;

[0038] Specifically, feature extraction of the video stream collected by the target control ball can be divided into two steps: primary feature extraction and secondary feature extraction. This two-step feature extraction method can effectively improve the accuracy and efficiency of the monitoring and analysis system, ensuring that more detailed and effective information can be obtained.

[0039] Step S205, performing feature fusion on the primary video stream feature information and the secondary video stream feature information to obtain video stream feature fusion information;

[0040] Specifically, feature fusion of the primary video stream feature information and the secondary video stream feature information is a key task, the purpose of which is to combine features at different levels to enhance the overall feature expression and recognition effect. Feature fusion can effectively improve the robustness and accuracy of the model, and by combining multi-level information, it helps the system understand the video content more comprehensively. This step has important application value in computer vision, surveillance analysis, behavior recognition and other fields.

[0041] Step S206, performing feature detection on the video stream feature fusion information to generate object coordinates of a preset tracking object according to the feature detection result;

[0042] Through the above steps, feature detection can be effectively performed on the video stream feature fusion information, and the object coordinates of the preset tracking object can be generated, thereby realizing the monitoring and analysis of the target object.

[0043] Step S207, determining tracking and identification data according to the object coordinates, and generating the object motion trajectory according to the tracking and identification data;

[0044] Through the above steps, the object can be effectively tracked and identified and its motion trajectory can be generated. The accuracy of the data association and trajectory update algorithm in the tracking process is ensured to improve the reliability and accuracy of the final generated motion trajectory.

[0045] Step S208, adjusting the working state of the target control ball according to the object's motion trajectory and preset control rules.

[0046] Through the above steps, the working state of the target control ball can be flexibly adjusted according to the object's motion trajectory and the preset control rules. This not only improves the monitoring effect, but also enables timely response to emergencies and enhances the intelligence level of the system. Reasonable rule settings and real-time adjustment mechanisms can greatly improve the performance and responsiveness of the overall monitoring system.

[0047] It can be seen that the embodiment of the present application provides a method for adjusting the working state of a control ball, firstly positioning the control ball and performing network encoding operations according to the initial positioning data, then extracting features from the collected video stream and generating feature fusion information, then using a preset algorithm to detect the feature fusion information to generate the object coordinates of the preset tracking object in each feature image, then performing data association to generate the object motion trajectory, and finally adjusting the working state of the target control ball based on the object motion trajectory and the preset control rules, realizing linkage processing between control balls, improving the degree of intelligence and the accuracy of tracking results, so that the control ball can accurately identify and track the target object in a complex monitoring environment, and has high practicality. This solves the technical problem that the control ball in the related art can only identify and track the target in a simple monitoring environment, but is easily affected by external environmental factors in a complex environment, resulting in inaccurate target identification.

[0048] As a possible implementation method, positioning the control ball according to the usage scenario and the monitored object to obtain the initial positioning data of the control ball includes the following steps:

[0049] Step S301, determining the initial placement position of the control ball in the target construction area according to the usage scenario;

[0050] Step S302, determining whether the control ball is adjusted at the initial placement position according to the dynamic and static information of the monitored object, and obtaining confirmation information;

[0051] Step S303, when it is determined according to the determination information that the initial placement position needs to be adjusted, the initial placement position is adjusted according to the dynamic and static information of the monitored object to locate the position of the control ball.

[0052] As a possible implementation method, a feature extraction is performed on the video stream collected by the target control ball to obtain video stream feature information, including the following steps:

[0053] Step S401, collecting video of the monitored object through the built-in image acquisition device of the surveillance ball to obtain a video stream;

[0054] Step S402, performing image processing on the image frames in the video stream respectively to extract key points of the image frames;

[0055] Step S403, determining the similarity between the key points, and determining the geometric transformation relationship of the monitored object based on the similarity;

[0056] Step S404, obtaining primary video stream feature information according to the geometric transformation relationship.

[0057] Specifically, Figure 3It is a schematic diagram of the first feature extraction according to an embodiment of the present invention. In the embodiment of the present invention, the first feature extraction is specifically as follows: S1, image acquisition: the control ball acquires video images through its built-in camera; S2, key point extraction: the acquired images are processed, and the key points of the two images are extracted respectively. The mapping relationship is determined by calculating the similarity between these key points, and then the geometric transformation relationship is calculated; S3, 4G / 5G wireless transmission: 4G / 5G wireless transmission technology is used to transmit the video image to the back-end platform in real time; S4, portrait detection edge: portrait detection is performed using a deep learning algorithm to realize the marginalization of intelligent analysis; S5, product detection: product detection is performed through a deep learning algorithm; S6, video and data storage: images and data are stored for subsequent analysis and auditing; S7, intelligent analysis marginalization: the calculation is transferred from the platform to the front end to realize the marginalization of intelligent analysis and improve processing efficiency and real-time performance; S8, combined with job control: the image recognition processing content is combined with job control.

[0058] As a possible implementation method, performing secondary feature extraction on the video stream to obtain secondary video stream feature information includes the following steps:

[0059] Step S501, performing image preprocessing on each frame of the video stream to obtain a plurality of target preprocessed images corresponding to the target control ball;

[0060] Among them, the image preprocessing is specifically to use a filtering algorithm to filter each frame of the video stream collected by the target control ball to generate an initial preprocessed image; adjust the brightness of the initial preprocessed image according to the preset image brightness and perform denoising to generate an intermediate preprocessed image; perform image enhancement on the intermediate preprocessed image to generate a target preprocessed image.

[0061] Step S502, performing feature extraction on a plurality of target pre-processed images respectively to obtain object features of the monitored object;

[0062] Among them, generating the object coordinates corresponding to the feature image is specifically by respectively detecting the coordinates of the preset tracking object in each feature image of the feature data, generating the object coordinates corresponding to the feature image, the preset algorithm includes a template matching algorithm and a haar feature algorithm, and generating the object coordinates corresponding to the feature image is specifically: using the haar feature algorithm to extract features from each feature image of the feature data respectively to generate multiple image features, using an integral image to quickly calculate the feature value of each image feature, generating multiple initial feature values, using an adaboost algorithm to select the initial feature value that meets the preset selection standard, generating multiple target feature values, using the target feature value to train a preset initial cascade classifier to generate a target cascade classifier, using the target cascade classifier to perform sliding window target detection on the image features respectively, determining the target area, calculating the similarity of each position between the template image and the target area in the template matching algorithm, generating multiple similarities, and using the coordinates corresponding to the similarity that meets the preset similarity threshold as the coordinates of the preset tracking object in the feature image to generate the object coordinates corresponding to the feature image.

[0063] Step S503: Generate secondary video stream feature information based on all object features.

[0064] Through the above steps, secondary feature extraction of the video stream can be performed efficiently to obtain rich secondary video stream feature information, providing a solid foundation for subsequent intelligent applications.

[0065] As a possible implementation method, image preprocessing is performed on each frame of the video stream to obtain multiple target preprocessed images corresponding to the target control ball, including the following steps:

[0066] Step S601, filtering each frame of the image using a filtering algorithm to obtain an initial pre-processed image;

[0067] Step S602, adjusting the brightness of the initial preprocessed image to obtain the initial preprocessed image after the brightness is adjusted;

[0068] Step S603, performing denoising on the initial pre-processed image after brightness adjustment to generate an intermediate pre-processed image;

[0069] Step S604: performing image enhancement processing on the intermediate pre-processed image to generate a target pre-processed image.

[0070] It can be seen that through image preprocessing, the target is clearer, which helps to improve the performance of subsequent algorithms.

[0071] Through the above steps, each frame in the video stream can be effectively preprocessed to obtain multiple high-quality target preprocessed images, providing reliable image data support for target tracking and recognition.

[0072] As a possible implementation method, feature detection is performed on the video stream feature fusion information to generate object coordinates of a preset tracking object according to the feature detection result, including the following steps:

[0073] Step S701, using a feature algorithm to extract features from each feature image corresponding to the video stream feature fusion information to obtain multiple image features;

[0074] Step S702, calculating the eigenvalues ​​of multiple image features using the integral image to obtain multiple initial eigenvalues;

[0075] Step S703, selecting initial eigenvalues ​​that meet a preset selection criterion to generate a plurality of target eigenvalues;

[0076] Step S704, generating object coordinates according to multiple target feature values.

[0077] Through the above steps, key features in the video stream can be efficiently converted into accurate coordinates that can be used for object tracking and identification, facilitating the implementation of various intelligent monitoring and analysis applications.

[0078] As a possible implementation method, the tracking and identification data is determined according to the coordinates of the object, including: adjusting the working state of the target control ball according to the object's motion trajectory and preset control rules to obtain the tracking and identification data.

[0079] Furthermore, generating the object motion trajectory through data association specifically involves using all object coordinates to perform data association to generate the object motion trajectory.

[0080] It can be seen that the system can adjust the working state of the control ball in real time and dynamically according to the object's motion trajectory and preset control rules, so as to obtain tracking and identification data more effectively. This not only improves the accuracy and efficiency of monitoring, but also provides strong support for subsequent data analysis and decision-making.

[0081] It can be seen that the present invention discloses a method for adjusting the working state of a control ball, firstly positioning the control ball and performing a network coding operation according to the initial positioning data, then extracting features from the collected video stream and generating feature fusion information, then using a preset algorithm to detect the feature fusion information to generate the object coordinates of the preset tracking object in each feature image, then performing data association to generate the object motion trajectory, and finally adjusting the working state of the target control ball based on the object motion trajectory and the preset control rules, realizing linkage processing between control balls, improving the degree of intelligence and the accuracy of tracking results, so that the control ball can accurately identify and track the target object in a complex monitoring environment, and has high practicality. This solves the technical problem that the control ball in the related art can only identify and track the target from a simple monitoring environment, but is easily affected by external environmental factors in a complex environment, resulting in inaccurate target identification.

[0082] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0083] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0084] According to an embodiment of the present invention, a device for adjusting the working state of the control ball is also provided. Figure 4 is a schematic diagram of a working state adjustment device of a control ball according to an embodiment of the present invention, Figure 4 As shown, the device includes: an acquisition unit 10, a positioning unit 20, a generation unit 30, an extraction unit 40, a fusion unit 50, a detection unit 60, a trajectory generation unit 70 and an adjustment unit 80.

[0085] An acquisition unit, used to acquire a usage scenario and a monitoring object of the control ball, wherein the usage scenario is an application scenario of the control ball, and the monitoring object is a monitoring target of the control ball;

[0086] Specifically, by using scenarios, it is determined that the control ball needs to be placed in the best position to cover the entire construction area in order to monitor the construction progress and safety situation in real time. The position is adjusted according to the monitoring target. If the monitoring target is a moving target, such as personnel and equipment on the construction site, then the control ball needs to flexibly adjust the angle and position to maintain continuous monitoring of the target.

[0087] Through the above steps, the combination of application scenarios and monitoring objects can be realized. The control ball can provide diversified monitoring solutions for different fields, thereby improving the efficiency and accuracy of the Internet of Things and intelligent monitoring.

[0088] The positioning unit is used to locate the control ball according to the usage scenario and the monitored object, and obtain the initial positioning data of the control ball;

[0089] Specifically, the initial positioning data, including the coordinates, number, status and other information of the control ball, is obtained and sorted. The initial data can be formatted and standardized to ensure the correctness and consistency of the data. A suitable encoding algorithm (such as hash encoding, bit encoding, etc.) is selected to encode the control ball. According to the selected algorithm, the processed data is encoded to generate a unique target control ball identification code. The encoded target control ball information can also be uploaded to the server or database for subsequent real-time monitoring and management.

[0090] A generating unit, used for performing a network encoding operation on the control ball according to the initial positioning data to generate a target control ball;

[0091] An extraction unit, used for performing a primary feature extraction on the video stream collected by the target control ball to obtain primary video stream feature information, and performing a secondary feature extraction on the video stream to obtain secondary video stream feature information;

[0092] Specifically, feature extraction of the video stream collected by the target control ball can be divided into two steps: primary feature extraction and secondary feature extraction. This two-step feature extraction method can effectively improve the accuracy and efficiency of the monitoring and analysis system, ensuring that more detailed and effective information can be obtained.

[0093] A fusion unit, used for fusing the primary video stream feature information and the secondary video stream feature information to obtain video stream feature fusion information;

[0094] Specifically, feature fusion of the primary video stream feature information and the secondary video stream feature information is a key task, the purpose of which is to combine features at different levels to enhance the overall feature expression and recognition effect. Feature fusion can effectively improve the robustness and accuracy of the model, and by combining multi-level information, it helps the system understand the video content more comprehensively. This step has important application value in computer vision, surveillance analysis, behavior recognition and other fields.

[0095] A detection unit, used to perform feature detection on the video stream feature fusion information, so as to generate object coordinates of a preset tracking object according to the feature detection result;

[0096] Through the above steps, feature detection can be effectively performed on the video stream feature fusion information, and the object coordinates of the preset tracking object can be generated, thereby realizing the monitoring and analysis of the target object.

[0097] A trajectory generating unit, used to determine tracking identification data according to the coordinates of the object, and to generate a motion trajectory of the object according to the tracking identification data;

[0098] Through the above steps, the object can be effectively tracked and identified and its motion trajectory can be generated. The accuracy of the data association and trajectory update algorithm in the tracking process is ensured to improve the reliability and accuracy of the final generated motion trajectory.

[0099] The adjustment unit is used to adjust the working state of the target control ball according to the object's motion trajectory and preset control rules.

[0100] Through the above steps, the working state of the target control ball can be flexibly adjusted according to the object's motion trajectory and the preset control rules. This not only improves the monitoring effect, but also enables timely response to emergencies and enhances the intelligence level of the system. Reasonable rule settings and real-time adjustment mechanisms can greatly improve the performance and responsiveness of the overall monitoring system.

[0101] A device for adjusting the working state of the control ball is also provided. Figure 4 is a schematic diagram of a working state adjustment device of a control ball according to an embodiment of the present invention, Figure 4 As shown, the device includes: an acquisition unit 10, a positioning unit 20, a generation unit 30, an extraction unit 40, a fusion unit 50, a detection unit 60, a trajectory generation unit 70 and an adjustment unit 80.

[0102] The embodiment of the present application provides a device for adjusting the working state of a control ball, which includes: an acquisition unit, a positioning unit, a generation unit, an extraction unit, a fusion unit, a detection unit, a trajectory generation unit and an adjustment unit. The device first locates the position of the control ball and performs a network encoding operation according to the initial positioning data, then extracts features from the collected video stream and generates feature fusion information, then uses a preset algorithm to detect the feature fusion information to generate the object coordinates of the preset tracking object in each feature image, then performs data association to generate the object motion trajectory, and finally adjusts the working state of the target control ball based on the object motion trajectory and the preset control rules, realizes linkage processing between control balls, improves the degree of intelligence and the accuracy of tracking results, and enables the control ball to accurately identify and track the target object in a complex monitoring environment, with high practicality. This solves the technical problem that the control ball in the related art can only identify and track the target from a simple monitoring environment, but is easily affected by external environmental factors in a complex environment, resulting in inaccurate target recognition.

[0103] It should be noted here that the above-mentioned acquisition unit 10, positioning unit 20, generation unit 30, extraction unit 40, fusion unit 50, detection unit 60, trajectory generation unit 70 and adjustment unit 80 correspond to steps S201 to S208 in the above-mentioned embodiments, and the eight modules are the same as the instances and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0104] As a possible implementation manner, the positioning device includes: a first determination module, a second determination module and a positioning module.

[0105] The first determination module is used to determine the initial placement position of the control ball in the target construction area according to the usage scenario;

[0106] The second determination module is used to determine whether the control ball is adjusted at the initial placement position according to the dynamic and static information of the monitored object to obtain determination information;

[0107] The positioning module is used to adjust the initial placement position according to the dynamic and static information of the monitored object when it is determined according to the determined information that the initial placement position needs to be adjusted, so as to locate the control ball.

[0108] As a possible implementation manner, the extraction unit includes: a collection module, a processing module, a relationship determination module and a primary information module.

[0109] The acquisition module is used to acquire video of the monitored object through the built-in image acquisition device of the control ball to obtain a video stream;

[0110] A processing module, used for performing image processing on image frames in the video stream respectively to extract key points of the image frames;

[0111] A relationship determination module is used to determine the similarity between key points and determine the geometric transformation relationship of the monitored object based on the similarity;

[0112] The primary information module is used to obtain primary video stream feature information based on geometric transformation relationships.

[0113] As a possible implementation manner, the extraction unit further includes: a preprocessing module, a feature extraction module and a secondary information module.

[0114] A preprocessing module is used to perform image preprocessing on each frame of the video stream to obtain multiple target preprocessing images corresponding to the target control ball;

[0115] A feature extraction module is used to extract features from a plurality of target pre-processed images respectively to obtain object features of the monitored object;

[0116] The secondary information module is used to generate secondary video stream feature information based on all object features.

[0117] Through the above steps, secondary feature extraction of the video stream can be performed efficiently to obtain rich secondary video stream feature information, providing a solid foundation for subsequent intelligent applications.

[0118] As a possible implementation manner, the preprocessing module includes: a filtering submodule, an adjustment submodule, a denoising submodule and an enhancement submodule.

[0119] The filtering submodule is used to filter each frame of the image through a filtering algorithm to obtain an initial pre-processed image;

[0120] The adjustment submodule is used to adjust the brightness of the initial preprocessed image to obtain the initial preprocessed image after the brightness is adjusted;

[0121] A denoising submodule, used for denoising the initial pre-processed image after adjusting the brightness, to generate an intermediate pre-processed image;

[0122] The enhancement submodule is used to perform image enhancement processing on the intermediate preprocessed image to generate a target preprocessed image.

[0123] Through the above steps, each frame in the video stream can be effectively preprocessed to obtain multiple high-quality target preprocessed images, providing reliable image data support for target tracking and recognition.

[0124] As a possible implementation manner, the detection unit includes: an image feature extraction module, a calculation module, a feature value generation module and a coordinate generation module.

[0125] An image feature extraction module is used to extract features from each feature image corresponding to the video stream feature fusion information using a feature algorithm to obtain multiple image features;

[0126] A calculation module, used for calculating the eigenvalues ​​of multiple image features using the integral image to obtain multiple initial eigenvalues;

[0127] An eigenvalue generation module is used to select initial eigenvalues ​​that meet preset selection criteria to generate multiple target eigenvalues;

[0128] The coordinate generation module is used to generate object coordinates according to multiple target feature values.

[0129] Through the above steps, key features in the video stream can be efficiently converted into accurate coordinates that can be used for object tracking and identification, facilitating the implementation of various intelligent monitoring and analysis applications.

[0130] As a possible implementation manner, the trajectory generating unit includes: a state adjustment module.

[0131] The state adjustment module is used to adjust the working state of the target control ball according to the object's motion trajectory and preset control rules to obtain tracking and identification data.

[0132] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein the program executes any one of the above-mentioned methods for adjusting the working state of a control ball.

[0133] According to another aspect of an embodiment of the present invention, a processor is further provided, and the processor is used to run a program, wherein when the program is run, any one of the above-mentioned methods for adjusting the working state of the control ball is executed.

[0134] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0135] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0136] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0137] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0138] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0139] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0140] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for adjusting the working state of a control ball, characterized in that: include: Acquire the usage scenario and monitoring object of the control ball, wherein the usage scenario is the application scenario of the control ball, and the monitoring object is the monitoring target of the control ball; Positioning the control ball according to the usage scenario and the monitored object to obtain initial positioning data of the control ball; Performing a network encoding operation on the control ball according to the initial positioning data to generate a target control ball; Performing a primary feature extraction on the video stream collected by the target control ball to obtain primary video stream feature information, and performing a secondary feature extraction on the video stream to obtain secondary video stream feature information; Performing feature fusion on the primary video stream feature information and the secondary video stream feature information to obtain video stream feature fusion information; Performing feature detection on the video stream feature fusion information to generate object coordinates of a preset tracking object according to the feature detection result; Determining tracking and identification data according to the object coordinates, and generating a motion trajectory of the object according to the tracking and identification data; The working state of the target control ball is adjusted according to the movement trajectory of the object and the preset control rules.

2. The working state adjustment method of the control ball according to claim 1, characterized in that: Positioning the control ball according to the usage scenario and the monitored object to obtain initial positioning data of the control ball includes: Determine the initial placement position of the control ball in the target construction area according to the usage scenario; Determine whether the control ball is adjusted at the initial placement position according to the motion and stillness information of the monitored object, and obtain confirmation information; When it is determined according to the determination information that the initial placement position needs to be adjusted, the initial placement position is adjusted according to the motion and stillness information of the monitored object to locate the position of the control ball.

3. The working state adjustment method of the control ball according to claim 1, characterized in that: Performing a feature extraction on the video stream collected by the target control ball to obtain video stream feature information includes: Capturing video of the monitored object through the built-in image acquisition device of the control ball to obtain the video stream; Performing image processing on the image frames in the video stream respectively to extract key points of the image frames; Determining the similarity between the key points, and determining the geometric transformation relationship of the monitored object based on the similarity; The primary video stream feature information is obtained according to the geometric transformation relationship.

4. The working state adjustment method of the control ball according to claim 1, characterized in that: Performing secondary feature extraction on the video stream to obtain secondary video stream feature information includes: Performing image preprocessing on each frame of the video stream to obtain a plurality of target preprocessed images corresponding to the target control ball; Performing feature extraction on the plurality of target preprocessed images respectively to obtain object features of the monitored object; The secondary video stream feature information is generated according to all the object features.

5. The working state adjustment method of the control ball according to claim 4 is characterized in that: Performing image preprocessing on each frame of the video stream to obtain a plurality of target preprocessed images corresponding to the target control ball, including: Performing filtering processing on each frame of the image by using a filtering algorithm to obtain an initial pre-processed image; Adjusting the brightness of the initial preprocessed image to obtain the initial preprocessed image after adjusting the brightness; Performing denoising on the initial preprocessed image after brightness adjustment to generate an intermediate preprocessed image; Perform image enhancement processing on the intermediate preprocessed image to generate the target preprocessed image.

6. The working state adjustment method of the control ball according to claim 1, characterized in that: Performing feature detection on the video stream feature fusion information to generate object coordinates of a preset tracking object according to the feature detection result, including: Using a feature algorithm to extract features from each feature image corresponding to the video stream feature fusion information to obtain multiple image features; Calculating the eigenvalues ​​of the plurality of image features using the integral image to obtain a plurality of initial eigenvalues; Selecting initial eigenvalues ​​that meet a preset selection criterion to generate a plurality of target eigenvalues; The object coordinates are generated according to the multiple target feature values.

7. The working state adjustment method of the control ball according to claim 1, characterized in that: Determining tracking and identification data according to the object coordinates includes: The working state of the target control ball is adjusted according to the object's motion trajectory and preset control rules to obtain the tracking and identification data.

8. A working state adjustment device for a control ball, characterized in that: The device comprises: An acquisition unit, used to acquire a usage scenario and a monitoring object of the control ball, wherein the usage scenario is an application scenario of the control ball, and the monitoring object is a monitoring target of the control ball; A positioning unit, used to locate the control ball according to the usage scenario and the monitored object, and obtain initial positioning data of the control ball; A generating unit, configured to perform a network encoding operation on the control ball according to the initial positioning data to generate a target control ball; An extraction unit, configured to perform a primary feature extraction on the video stream collected by the target control ball to obtain primary video stream feature information, and perform a secondary feature extraction on the video stream to obtain secondary video stream feature information; A fusion unit, used for performing feature fusion on the primary video stream feature information and the secondary video stream feature information to obtain video stream feature fusion information; A detection unit, configured to perform feature detection on the video stream feature fusion information, so as to generate object coordinates of a preset tracking object according to the feature detection result; A trajectory generating unit, configured to determine tracking identification data according to the object coordinates, and generate a motion trajectory of the object according to the tracking identification data; The adjustment unit is used to adjust the working state of the target control ball according to the movement trajectory of the object and the preset control rules.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the working state adjustment method of the control ball according to any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the working state adjustment method of the control ball described in any one of claims 1 to 7.

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