Pixel adaptive video monitoring system and method based on intelligent Dongle

Abstract

The invention relates to the field of video monitoring, in particular to a pixel self-adaptive video monitoring system and method based on intelligent Dongle. The system is highly integrated with Dongle form equipment, and the intelligent level, the resource utilization efficiency and the operation convenience of the video monitoring system are remarkably improved. Through the functions of pixel adaptive calling, AI intelligent analysis, seamless integration of local and cloud storage and the like, the requirements of a modern video monitoring system for high-definition image quality and intelligent analysis are met, and the monitoring experience of a user and the operation efficiency of the system are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of video monitoring, and in particular to a pixel adaptive video monitoring system and a monitoring method based on an intelligent Dongle. Background Art

[0002] With the rapid development of science and technology, video surveillance systems have become an important part of public safety, production management and family safety. Traditional video surveillance systems mostly use network video recorder (NVR) architecture, which collects video data through fixed cameras and stores, decodes and displays it through NVR devices. However, with the continuous improvement of the requirements for monitoring systems, the traditional NVR architecture has exposed the following significant deficiencies: 1. The system is bulky and inflexible: Traditional NVR systems are usually designed as large rack-mounted devices that contain multiple hard disks, network interfaces, and complex hardware components. These systems not only take up a lot of physical space during deployment, but also require dedicated cabinets and stable power supply conditions. Especially in scenarios with limited space such as small offices, homes, or retail stores, bulky equipment will cause inconvenience in installation and use. In addition, traditional NVR systems have poor mobility. Once installed, it is difficult to achieve flexible layout adjustments or equipment relocation.

[0003] 2. Complex installation and configuration, high technical threshold: The installation of NVR system involves complex wiring work, including network connection between camera and NVR host, hard disk installation and configuration, network configuration, etc. These operations usually require professional technicians to perform, which increases the deployment cost and time of the system. For small and medium-sized enterprises or home users, relying on professionals for installation and maintenance significantly increases the threshold for use and reduces the popularity of the system.

[0004] 3. Lack of intelligent analysis capabilities and inability to meet modern needs: Modern surveillance systems no longer rely solely on video storage and playback. More application scenarios require the system to have intelligent analysis capabilities, such as personnel detection, abnormal behavior recognition, object tracking, etc. However, the main function of traditional NVR systems is the storage and management of video data, and lacks the ability to deeply analyze video content. Even if some analysis functions are introduced in some high-end NVR devices, they still cannot meet the high demand for real-time intelligent analysis and response in modern surveillance scenarios. This defect limits the application scope of the surveillance system, especially in scenarios that require rapid response and intelligent decision-making.

[0005] 4. Inefficient resource utilization and inability to dynamically adjust according to demand: Traditional NVR systems are usually fixed configurations and cannot be dynamically adjusted according to the resolution of the display or the actual decoding capability of the system. This rigid configuration leads to inefficient resource utilization. For example, when the resolution of the camera is much higher than the NVR decoding capability, the quality of video playback will be greatly reduced and the image quality will be poor. In addition, when the system processes multiple video sources, it cannot effectively allocate the bandwidth and resolution of each video stream, resulting in bandwidth waste or insufficient decoding capability. This unreasonable resource utilization not only increases the operating cost of the system, but also affects the user experience.

[0006] 5. Rigid storage management, unable to achieve seamless integration between local and cloud storage: Traditional NVR systems mostly rely on local storage, and users need to continuously expand hard disk capacity to meet the increasing demand for monitoring data. This storage mode not only takes up a lot of physical space, but also requires frequent replacement or management of hard disks. In the modern network environment, cloud storage has gradually become the mainstream storage method, with the advantages of elastic expansion and data security. However, traditional NVR systems often lack cloud storage integration functions and cannot achieve seamless switching and management between local and cloud storage. This storage method limits the scalability and security of the system in the era of big data.

[0007] 6. Inadequate adaptability to application scenarios with high real-time requirements: In application scenarios that require real-time and efficient response, such as public safety and factory production monitoring, traditional NVR systems cannot provide fast response and real-time data processing due to the lack of intelligent analysis capabilities and efficient resource allocation mechanisms. This results in the system being unable to take preventive or response measures in a timely manner when faced with emergencies, affecting the overall monitoring effect.

[0008] In order to make up for the shortcomings of the above-mentioned traditional NVR systems, the industry has begun to explore more compact, intelligent and efficient monitoring solutions. For example, the combination of portable streaming devices and NVR functions can reduce the size of the system and simplify the installation process. In addition, with the development of artificial intelligence (AI) technology, embedding AI analysis modules into the monitoring system can greatly improve the intelligence level of the system and realize functions such as personnel detection, behavior analysis and automatic alarm triggering.

[0009] At the same time, the introduction of pixel adaptive technology can dynamically adjust the resolution of the video stream according to the resolution and decoding capability of the display, achieve more efficient resource utilization, reduce bandwidth waste, and ensure video quality. With the support of cloud computing and big data technology, the integration of local storage and cloud storage makes the management of monitoring data more flexible, and users can choose local or cloud storage mode according to their needs. Summary of the invention

[0010] In order to solve the above technical problems, the purpose of the present invention is to provide a pixel adaptive video surveillance system based on intelligent Dongle. The highly integrated Dongle-type device of the system significantly improves the intelligence level, resource utilization efficiency and operation convenience of the video surveillance system. Through functions such as pixel adaptive retrieval, AI intelligent analysis, and seamless integration of local and cloud storage, it not only meets the needs of modern video surveillance systems for high-definition image quality and intelligent analysis, but also greatly improves the user's monitoring experience and the system's operating efficiency.

[0011] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: A pixel adaptive video monitoring system based on intelligent Dongle, characterized by comprising: At least one network camera for collecting video data; A display for displaying video images; An NVC (Net Video Cast) Dongle device, the NVC Dongle device having a pixel adaptive call function, for dynamically adjusting the video stream resolution requested from the network camera according to the display resolution and the decoding capability of the NVC Dongle device; the NVC Dongle device comprises: HDMI interface, used to connect to a display; A decoding module, used to process the received video data and output it to the display via HDMI; Wireless transmission module, used to receive network camera video data and communicate with the cloud; A storage module, used for locally storing video data; AI analysis module, used to perform video content analysis; Power module, used to power the equipment; And a control module, which is connected to the decoding module, the wireless transmission module, the storage module and the AI ​​analysis module, and is used to receive the video data of the wireless transmission module and send the data to the decoding module, the storage module and the AI ​​analysis module.

[0012] Preferably, the pixel adaptive calling function is implemented by comprising the following steps: 1) Detect the resolution of the connected display; 2) Compare the detected display resolution with the decoding capability of the NVC Dongle; 3) Based on the comparison results, select the video stream retrieval mode in one of the following three situations: a. When the display resolution is not greater than the NVC Dongle decoding capability, the video stream is retrieved based on the display resolution; b. When the display resolution is greater than the NVC Dongle decoding capability, the video stream is retrieved based on the NVC Dongle decoding capability; c. When using multiple overlapping display windows and the total pixels exceed the NVC Dongle decoding capability, the video stream resolution of each window is adjusted proportionally.

[0013] Preferably, the NVC Dongle device also includes a cloud storage integration function, which is used to: a. automatically synchronize locally stored video data to the cloud; b. download historical video data from the cloud and decode and display it; c. automatically adjust local and cloud storage strategies according to storage space and network conditions.

[0014] Preferably, the AI ​​analysis module can perform one or more of the following functions: a. personnel detection and identification; b. object detection and tracking; c. abnormal behavior detection; d. triggering intelligent alarms based on AI analysis results; e. providing AI analysis capabilities for remote cameras that do not have AI functions; f. real-time overlay of AI analysis results on the video screen for display.

[0015] Preferably, the wireless transmission module adopts a Wi-Fi module and / or a Bluetooth module.

[0016] Preferably, the power module adopts a rechargeable battery and / or a USB interface.

[0017] Preferably, the USB interface adopts one of a Mini interface, a Micro interface, a Type-C interface and a Lightning Dock interface.

[0018] Furthermore, the present invention also discloses a pixel adaptive video monitoring method based on intelligent Dongle, comprising the following steps: 1) Connect the monitor via the HDMI interface of the NVC Dongle device; 2) Receive video data from the network camera through the Wi-Fi module; 3) Detect display resolution and calculate available decoding capabilities; 4) Based on the display resolution and decoding capability, use the pixel adaptive acquisition method to request a video stream with appropriate resolution from the network camera; 5) Receive, decode and display video data; 6) Store video data in local storage module and cloud storage at the same time; 7) Use AI analysis module to perform real-time analysis of video content.

[0019] Preferably, the method also includes the following steps: a. automatically adjusting the resolution and frame rate of the video stream according to the AI ​​analysis results; b. automatically improving the video quality and storage priority of the relevant cameras when an abnormal event is detected.

[0020] Preferably, the method further comprises the following steps: a. regularly checking the usage of local storage space; b. automatically transferring older video data to cloud storage when local storage space is insufficient; c. automatically deleting video data that has exceeded the retention period according to the policy set by the user.

[0021] Due to the adoption of the above technical solutions, the present invention has the following characteristics: 1. Small size and easy deployment: Through the highly integrated intelligent Dongle device, the huge volume of the traditional NVR system is reduced, and plug-and-play convenient deployment is realized, which is suitable for small offices, families and other scenes. 2. Resource optimization and improved video quality: The pixel adaptive call function can dynamically adjust the video stream according to the display resolution and decoding capability, maximize the use of bandwidth and decoding capability, and ensure video quality and fluency. 3. Intelligent analysis and real-time response: The integrated AI analysis module can realize intelligent analysis such as personnel detection and abnormal behavior recognition, and trigger real-time alarms to improve the security and responsiveness of the monitoring system. 4. Flexible storage and data security: Supports seamless integration of local and cloud storage, realizes flexible data management and storage strategies, and ensures data security and long-term preservation. 5. Reduce costs and save resources: Through intelligent resource allocation, bandwidth and storage costs are reduced, which is suitable for small and medium-sized enterprises and individual users, and realizes efficient monitoring solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Schematic diagram of the system structure of the present invention; wherein: 1. Network camera; 2. Display; 3. NVC Dongle device.

[0023] Figure 2 This is a schematic diagram of the NVC Dongle device structure; where: 31. HDMI male connector, 32. Wi-Fi module, 33. Decoding module, 34. Control module, 35. Storage module, 36. AI analysis module.

[0024] Figure 3 The figure is a flowchart of the pixel adaptive retrieval method. DETAILED DESCRIPTION

[0025] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with 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 are within the scope of protection of the present invention.

[0026] like Figure 1 A pixel adaptive video surveillance system based on an intelligent Dongle is shown, comprising: At least one network camera for collecting video data; A display for displaying video images; An NVC (Net Video Cast) Dongle device, wherein the NVC Dongle device has a pixel adaptive calling function, which is used to dynamically adjust the resolution of the video stream requested from the network camera according to the display resolution and the decoding capability of the NVC Dongle device; like Figure 2 As shown, the NVC Dongle consists of an HDMI male connector and a device body, the HDMI male connector and the device body are connected to each other through an HDMI cable, and the device body includes an outer shell and the following modules encapsulated in the outer shell: A decoding module, used to process the received video data and output it to the display via HDMI; Wi-Fi module, used to receive camera video data and communicate with the cloud; A storage module, used for locally storing video data; AI analysis module, used to perform video content analysis; USB interface, used to power the device; And a control module, which is connected to the decoding module, the wireless transmission module, the storage module and the AI ​​analysis module, and is used to receive the video data of the wireless transmission module and send the data to the decoding module, the storage module and the AI ​​analysis module.

[0027] like Figure 3 As shown, the pixel adaptive calling function is implemented by including the following steps: 1) Detect the resolution of the connected display; 2) Compare the detected display resolution with the decoding capability of the NVC Dongle; 3) Based on the comparison results, select the video stream retrieval mode in one of the following three situations: a. When the display resolution is not greater than the NVC Dongle decoding capability, the video stream is retrieved based on the display resolution; b. When the display resolution is greater than the NVC Dongle decoding capability, the video stream is retrieved based on the NVC Dongle decoding capability; c. When using multiple overlapping display windows and the total pixels exceed the NVC Dongle decoding capability, the video stream resolution of each window is adjusted proportionally.

[0028] The decoding module is responsible for processing the compressed video data received from the camera and converting it into a format that can be viewed on the display. Video data is usually compressed during transmission to reduce bandwidth usage and storage requirements. The main task of the decoding module is to decode these compressed data and restore them to the original video image. A dedicated hardware decoding chip can be used, which can process a variety of video encoding formats (such as H.264, H.265, etc.), with high efficiency and low latency; supports simultaneous decoding of multiple video streams, suitable for monitoring scenarios of multiple cameras; and outputs video signals through the HDMI interface, supports high-definition (1080p) and higher resolutions, and ensures the clarity and smoothness of the video image.

[0029] The Wi-Fi module is responsible for wireless data communication with the camera and the cloud server. It is an important bridge for the device to receive video data and synchronize with the cloud. The Wi-Fi module can use dual-band Wi-Fi, support 2.4GHz and 5GHz frequency bands, provide more stable and high-speed wireless connection, and use Advanced Encryption Standard (AES) and Wi-Fi Protected Access (WPA / WPA2) protocols to ensure the security of data transmission; support MIMO (Multiple Input Multiple Output) technology to enhance signal coverage and transmission rate.

[0030] The storage module is used to store the received video data locally. It ensures the persistence of the video data and quick access when needed. The storage module supports a variety of storage media, such as SSD (solid state drive) and HDD (hard disk); adopts high-speed interface (such as SATAIII, NVMe) and cache technology to ensure fast writing and reading of video data; and supports RAID (redundant array of independent disks) technology to provide data redundancy and fault recovery capabilities.

[0031] The AI ​​analysis module is responsible for real-time analysis of received video data. It can identify specific objects, behaviors, and events in the video and generate corresponding alarms or analysis reports. Deep learning algorithms can be used to perform complex video content analysis, such as face recognition, behavior detection, abnormal event recognition, etc. Typical functions are as follows: 1. Person detection and identification: Detect people in the video and optionally identify them; 2. Object detection and tracking: Identify and track specific objects in the video; 3. Abnormal behavior detection: analyze and mark possible abnormal behaviors; 4. Intelligent alarm: trigger intelligent alarm based on AI analysis results; 5. Camera AI capability expansion: Provide AI analysis capabilities for remote cameras that do not have AI functions; 6. Overlay display of analysis results: The AI ​​analysis results can be overlaid on the video screen in real time.

[0032] The USB interface is used to power the device, ensuring that the device can work properly at any time. It can also expand other peripherals, such as external storage devices or input devices.

[0033] The control module is the core control unit of the entire device, responsible for managing and coordinating data transmission and processing between various functional modules. It ensures that all parts of the device work in coordination to achieve the reception, decoding, storage, analysis and synchronization of video data. A high-performance embedded processor can be used to support a multi-tasking operating system, which can manage the execution of multiple tasks at the same time and integrate multiple interfaces (such as I2C, SPI, UART, etc.) to facilitate connection and communication with various functional modules.

[0034] The core functions of the present invention are: 1. Video reception and decoding: Receive multi-channel camera videos via Wi-Fi, and decode and display them in real time; 2. Local storage: store the received video data in the built-in storage module; 3. Cloud Storage Integration: Local to cloud synchronization: automatically synchronize locally stored video data to cloud storage; Download from cloud to local: support downloading historical video data from the cloud and decoding and displaying; Real-time cloud backup: You can choose to store the received video data locally and in the cloud simultaneously in real time; Intelligent storage management: Automatically adjust local and cloud storage strategies based on storage space and network conditions.

[0035] The above-mentioned video surveillance system provides a pixel adaptive video surveillance method based on intelligent Dongle, comprising the following steps: 1) Connect the monitor via the HDMI interface of the NVC Dongle device; 2) Receive video data from the network camera through the Wi-Fi module; 3) Detect display resolution and calculate available decoding capabilities; 4) Based on the display resolution and decoding capability, use the pixel adaptive acquisition method to request a video stream with appropriate resolution from the network camera; 5) Receive, decode and display video data; 6) Store video data in local storage module and cloud storage at the same time; 7) Use AI analysis module to perform real-time analysis of video content.

[0036] Furthermore, the method also includes the following steps: a. automatically adjusting the resolution and frame rate of the video stream according to the AI ​​analysis results; b. automatically improving the video quality and storage priority of the relevant cameras when an abnormal event is detected.

[0037] Furthermore, the method also includes the following steps: a. regularly checking the usage of local storage space; b. automatically transferring older video data to cloud storage when local storage space is insufficient; c. automatically deleting video data that has exceeded the retention period according to the policy set by the user.

[0038] The highly integrated surveillance camera video stream decoding, display and storage device of the present invention is particularly suitable for the following scenarios: 1. Small office: Due to its small size and easy installation, it is very suitable for small offices with limited space.

[0039] 2. Retail stores: Retail stores usually need to monitor the store situation in real time and save video records. This device can simplify installation and provide efficient monitoring and intelligent analysis.

[0040] 3. Home: Home users have high requirements on the appearance and usability of monitoring equipment. This device is plug-and-play, easy to configure and suitable for home environment.

[0041] 4. Mobile monitoring: The portability and high integration of this device make it very suitable for occasions where temporary monitoring is required, such as exhibitions and event sites.

[0042] 5. Remote monitoring: For scenarios that require remote access and management, the cloud storage and remote access functions provided by the device can meet the needs.

[0043] The above is a description of the embodiments of the present invention. Through the above description of the disclosed embodiments, professionals and technicians in the field can implement or use the present invention. Various modifications to these embodiments will be apparent to professionals and technicians in the field. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown in this article, but will conform to the widest range consistent with the principles and novelties disclosed herein.

Claims

1. A pixel adaptive video surveillance system based on intelligent Dongle, characterized by ,include: At least one network camera for collecting video data; A display for displaying video images; An NVC (Net Video Cast) Dongle device, wherein the NVC Dongle device has a pixel adaptive calling function, which is used to dynamically adjust the resolution of the video stream requested from the network camera according to the display resolution and the decoding capability of the NVC Dongle device; The NVC Dongle device includes: HDMI interface, used to connect to a display; A decoding module, used to process the received video data and output it to the display via HDMI; Wireless transmission module, used to receive network camera video data and communicate with the cloud; A storage module, used for locally storing video data; AI analysis module, used to perform video content analysis; Power module, used to power the equipment; And a control module, which is connected to the decoding module, the wireless transmission module, the storage module and the AI ​​analysis module, and is used to receive the video data of the wireless transmission module and send the data to the decoding module, the storage module and the AI ​​analysis module.

2. The system according to claim 1, characterized in that The pixel adaptive calling function is implemented by including the following steps: 1) Detect the resolution of the connected display; 2) Compare the detected display resolution with the decoding capability of the NVC Dongle; 3) Based on the comparison results, select the video stream retrieval mode in one of the following three situations: a. When the display resolution is not greater than the NVC Dongle decoding capability, the video stream is retrieved based on the display resolution; b. When the display resolution is greater than the NVC Dongle decoding capability, the video stream is retrieved based on the NVC Dongle decoding capability; c. When using multiple overlapping display windows and the total pixel count exceeds the NVC Dongle decoding capability, the video stream resolution of each window is adjusted proportionally.

3. The system according to claim 1, characterized in that The NVC Dongle device also includes a cloud storage integration function, which is used to: a. automatically synchronize locally stored video data to the cloud; b. download historical video data from the cloud and decode and display it; c. automatically adjust local and cloud storage strategies based on storage space and network conditions.

4. The system according to claim 1, characterized in that The AI ​​analysis module can perform one or more of the following functions: a. Personnel detection and identification; b. Object detection and tracking; c. Abnormal behavior detection; d. Triggering intelligent alarms based on AI analysis results; e. Providing AI analysis capabilities for remote cameras that do not have AI functions; f. Real-time overlay of AI analysis results on the video screen for display.

5. The system according to claim 1, characterized in that The wireless transmission module adopts a Wi-Fi module and / or a Bluetooth module.

6. According to the system of claim 1, the power module adopts a rechargeable battery and / or a USB interface.

7. According to the system of claim 1, the USB interface adopts one of a Mini interface, a Micro interface, a Type-C interface and a Lightning Dock interface.

8. A pixel adaptive video monitoring method based on intelligent Dongle, characterized in that: The following steps are involved: 1) Connect the monitor via the HDMI interface of the NVC Dongle device; 2) Receive video data from the network camera through the Wi-Fi module; 3) Detect display resolution and calculate available decoding capabilities; 4) Based on the display resolution and decoding capability, use the pixel adaptive acquisition method to request a video stream with appropriate resolution from the network camera; 5) Receive, decode and display video data; 6) Store video data in local storage module and cloud storage at the same time; 7) Use AI analysis module to perform real-time analysis of video content.

9. The method according to claim 5, characterized in that The following steps are also included: a. Automatically adjust the resolution and frame rate of the video stream based on AI analysis results; b. Automatically improve the video quality and storage priority of related cameras when an abnormal event is detected.

10. The method according to claim 5, characterized in that The following steps are also included: a. Regularly check the usage of local storage space; b. Automatically transfer older video data to cloud storage when local storage space is insufficient; c. Automatically delete video data that exceeds the retention period based on the policy set by the user.

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