100 results about "Feature tracking" patented technology

The embodiment of the invention provides a fish school individual tracking and monitoring method and system, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring video data, voiceprint features and environmental parameters of fishes; a multi-dimensional tensor data stream is constructed according to the video data, the voiceprint features and the environmental parameters, and the multi-dimensional tensor data stream comprises video frames, voiceprint vectors and environmental parameter vectors; adopting a preset hybrid architecture model to obtain a bounding box and a fish body visual feature vector according to the video frame; splicing the fish body visual feature vector, the voiceprint vector and the environment state vector into a fusion vector; calculating the similarity of the fusion vectors; screening the fusion vector according to the similarity; and performing fish school individual tracking operation according to the screened fusion vector. Compared with the common single feature tracking operation, the method, the device and the system provided by the invention have the advantages that the technical effect of more accurate counting tracking is realized due to the adoption of multiple dimensions.

The invention discloses an omnidirectional fisheye image feature tracking and extracting method based on a non-parallel virtual binocular, and the method comprises the steps: constructing a four-eye fisheye camera system, obtaining the relative pose transformation based on a pre-calibration external parameter, predicting the pose based on a uniform speed model or IMU pre-integration, and constructing a re-projection error function through common-view feature points. According to the invention, by constructing four groups of virtual binocular combinations, on the premise of not increasing the number of physical cameras, eight-view-angle equivalent coverage is realized, so that a high-overlapping common-view area is formed between adjacent cameras; on the basis of a pre-calibrated external parameter and an MEI fisheye model, a spherical epipolar line is directly generated for a distorted image to carry out matching search, and meanwhile, a time sequence-space two-dimensional fault-tolerant mechanism is put forward for challenges of shielding and environmental interference: according to a dynamic direction switching design, when a tracking feature number is lower than a threshold value due to shielding in a camera direction, the tracking feature number is smaller than the threshold value; the camera direction is actively switched to serve as a main tracking source, new feature points are supplemented at the same time, and system collapse caused by local view failure is avoided.

The invention discloses an equipment health feature extraction method and system based on a depth auto-encoder, and relates to the technical field of equipment feature extraction. According to the equipment health feature extraction method and system based on the depth auto-encoder, S1, equipment operation state data, environment interference data and channel attribute data are collected and preprocessed to construct a standardized stable feature expression set; s2, channel disturbance sensitivity is evaluated, and a signal retention strategy is dynamically selected; s3, analyzing signal disturbance components and switching a channel input form; s4, comprehensively evaluating a channel health fluctuation level, and controlling a stability identifier and a feature tracking strategy; and S5, jointly monitoring a feature offset and attribution relationship, correcting the feature mapping path and triggering abnormal attribution analysis. The problems that in an extreme environment, operation signals are prone to being disturbed, health feature extraction lacks a structural stability judgment mechanism, and abnormal states and environmental inducements are difficult to distinguish are solved.

The invention relates to the technical field of 3D visual inspection, in particular to a dynamic shielding area multi-view time sequence point cloud completion method and system in 3D visual inspection. The method comprises the following steps: collecting a multi-view time sequence image of a dynamic detection object for providing a data basis for feature point tracking; and carrying out Lucas-Kanade optical flow method tracking on the feature points, associating the feature points of different visual angles, describing a motion track of the feature points through an optical flow vector, carrying out cross-visual-angle and cross-time feature point matching, and establishing time sequence feature association to obtain time sequence feature points. According to the dynamic occlusion area multi-view time sequence point cloud completion method in 3D visual detection, 360-degree time sequence data of a dynamic detection object is acquired through multi-view time sequence image acquisition, and comprehensive basic data support is provided for subsequent feature tracking.

The invention relates to image processing, in particular to a complex environment-oriented power transmission line inspection image processing method, which comprises the following steps of: screening key frames with high geometric information content by quantitatively evaluating inter-frame motion amplitude and feature tracking quality; extracting features of each key frame by using a pre-trained visual model; performing intra-frame feature aggregation on the features of each key frame by using an intra-frame self-attention mechanism to obtain corresponding intra-frame feature representation; performing global feature aggregation on the intra-frame feature representations of all the key frames by using a global self-attention layer to obtain inter-frame feature representations; constructing a multi-task learning network, and carrying out end-to-end collaborative optimization on depth estimation, image defogging and high-level semantic segmentation; inputting the inter-frame feature representation into a multi-task learning network, and obtaining a restored clear image through deep fusion of scene depth information and image degradation priori; according to the method, the defect that the dual requirements of quality improvement and feature retention of the power transmission line inspection image in a complex environment cannot be met can be overcome.

The invention discloses a state estimation method based on mixed features and adaptive key frame selection, and belongs to the field of autonomous navigation of unmanned systems. The implementation method comprises the following steps of: acquiring a plurality of images, and synchronously acquiring inertial measurement data of a six-axis IMU (Inertial Measurement Unit); the method comprises the following steps: performing key frame decision on a current frame image according to inertial measurement data, and classifying the image into a key frame image and a non-key frame image: acquiring a key frame feature matching set through an entropy maximization self-supervision feature extractor and an image attention matcher, and constructing a visual reprojection error term based on triangulation map points; non-key frame feature points are extracted by adopting an improved corner detector, feature displacement is obtained through IMU constraint optical flow tracking, and a visual re-projection error and an IMU pre-integration residual term are generated; and based on a vision-inertia tight coupling optimization module solution system, combining a vision reprojection error, an IMU pre-integration residual error and a pneumatic constraint residual error to construct a tight coupling optimization problem, and solving an optimal pose state. The method is beneficial to navigation of the small-sized high-speed unmanned aerial vehicle.

The invention discloses a multi-modal feature tracking method based on a traffic scene, and relates to the technical field of traffic scene feature tracking, and the method comprises the steps: collecting traffic scene RGB image data and event camera data, and constructing a multi-modal time series data set; constructing a multi-modal feature tracking model; extracting a time sequence motion feature of the event camera data, and generating a displacement prediction vector; state prediction updating is carried out, and a tracking prediction result is generated; and continuously tracking the traffic target to generate motion tracking trajectory data. According to the invention, the technical problems of poor image quality, low tracking accuracy, weak single-mode tracking anti-interference capability and difficulty in adapting to high-frequency tracking requirements caused by the influence of factors such as illumination, weather, exposure and the like during traffic scene feature tracking in the prior art are solved; the technical effects of realizing collaborative feature tracking of the RGB image and the event camera multi-modal data in the traffic scene and improving the frequency, accuracy and anti-interference capability of feature tracking are achieved.

The application discloses a video instance segmentation method and system based on global feature enhancement, and belongs to the technical field of computer vision and deep learning, which comprises the following steps: inputting continuous video frames into a double-channel decoupling feature extraction module to obtain appearance and position features; inputting the two types of features into a tracker and a global feature memory updating module to dynamically update and obtain long-time sequence global context features; completing instance detection matching by the tracker, judging target state to obtain disappeared and newly appeared target queries; combining long-time sequence features to model current frame time sequence to generate enhanced features; finally, the tracker completes segmentation and tracking based on the enhanced features and the target queries, and outputs a segmentation mask sequence, an instance ID, a category ID and a tracking state. Through double-channel decoupling, double-memory block updating, multi-cost matching and multi-weight feature fusion, the application strengthens appearance feature representation and long-time sequence information utilization, and significantly improves segmentation accuracy and tracking robustness in complex scenes.

This invention relates to a global feature tracking method for time-varying data based on unsupervised segmentation. It constructs a volume data segmentation network to segment the input volume data and achieve global tracking. This invention introduces deep learning to segment volume data, separating feature domains from the background. This enables automatic and accurate tracking of specific features in complex data without any manual annotation, reducing tracking complexity and improving tracking accuracy. It introduces a global feature tracking method; by selecting a target feature, it can track the trajectories of features similar to the target feature across all time steps. Users can select any feature at any time step to track features of interest in time-varying data. It can track extracted features from a global perspective, avoiding tracking errors and defects caused by local tracking methods. Furthermore, it adds the tracking of spatiotemporally similar features, simultaneously tracking the complete paths of spatially similar features of the target feature, improving feature tracking accuracy.

The present invention discloses a VxWorks transport layer protocol fuzz testing method based on feature tracking. This method performs consistency checks on the message response flag features of the transport layer TCP protocol of the closed-source operating system VxWorks, thereby driving seed iterative mutation and dynamically guiding the fuzz testing process. A progressive fuzzification method that fuses feature relationships between messages is used to construct test cases with high acceptance rates and high complexity. A dual anomaly detection mechanism that combines client connection detection with system anomaly task detection based on WDB RPC is used to determine and detect abnormal states during the fuzz testing process. The present invention implements a lightweight, practical, and efficient fuzz testing method for the TCP protocol of the VxWorks operating system.

A surgical navigation system includes an extended reality head-mountable display (HMD). The surgical navigation system further includes a computer system comprising at least one processor, wherein the computer system is configured for performing operations. The operations include receiving camera images while a surgeon is wearing the HMD and performing a surgical procedure on a subject. The operations further include identifying at least one image feature depicted in the images. The operations further include tracking a location of at least one surgical object using the image feature. The operations further include sending one or more visual indicators to the HMD to display, to the surgeon, a target location for the surgical object for the surgical procedure. The target location includes a location external to the subject for the surgeon to use the surgical object to perform the surgical procedure on the subject.

The invention discloses a constraint visual servo method, system and device based on imitation learning and a medium, and belongs to the technical field of visual servo, and the method comprises the steps: obtaining target object data, mechanical arm data and camera data, and building a target object model, a mechanical arm model and an image feature model; modeling is conducted on the constraint of the mechanical arm through an imitation learning method, demonstration data are generated, and an optimized trajectory generation strategy is adopted to be combined with the demonstration data to generate a demonstration trajectory; calculating an image feature tracking error according to the demonstration trajectory, designing a cost function and a Hamiltonian function for constraining visual servo based on the image feature tracking error, and obtaining an optimal control strategy and optimal control input of the mechanical arm through the Hamiltonian function; and estimating the cost function by using a neural network estimator to obtain an estimated value of the optimal control strategy, and updating the neural network estimator according to the Hamiltonian function. The smooth mechanical arm track with the optimal cost can be generated without matrix inversion.

Provided herein is a method of detecting microorganisms in liquid samples using image features, such as time profiles of object light scattering intensity and time profiles of object position. Related methods, devices, systems, and other aspects are also provided.

This invention discloses a terahertz photoelectric image registration method using video feature following, comprising the following steps: image preprocessing; SURF-based first-frame image registration: calculating feature points of the matching first-frame radar image and first-frame optical image based on SURF features; video following feature extraction; and sequence image registration. This invention utilizes a video feature following method to transform the complex problem of heterogeneous image registration into a homogeneous image registration problem, reducing the problem complexity and improving registration accuracy.

The invention belongs to the technical field of industrial automation and safety monitoring, and particularly relates to an industrial door anti-pinch safety early warning method based on video stream moving target detection, which comprises the following steps of: S1, acquiring a real-time video stream of an industrial door area, performing preprocessing and feature tracking on the real-time video stream, mapping an image coordinate to a world coordinate system, and acquiring a real-time video stream of the industrial door area; acquiring a real-time position parameter and a movement speed parameter of the moving target; and S2, according to the motion speed parameter of the motion target and the direction relative to the door body, a radial approximation equivalent threat speed index is calculated, and the radial approximation equivalent threat speed index is used for representing the equivalent threat speed of sprint of the motion target to the door body. By establishing the multi-dimensional dynamic collision risk model, differentiated accurate response to high-speed forklifts and low-speed pedestrians is achieved, and the passing efficiency of the industrial door is maximized on the premise that safety is guaranteed.

Aspects of this technical solution can increase speed of processing and lower computational hardware complexity in motion detection, while maintaining integrity of motion detection across image frames. For example, in image-processing environments associated with autonomous or semi-autonomous navigation (e.g., driving, robotic navigation, etc.), a large volume of image data is to be rapidly and accurately processed to maintain reliable and up-to-date models of a physical environment. Thus, embodiments in accordance with this disclosure can provide high-speed and accurate motion detection of input frame data.

The invention relates to an image data sampling architecture for image feature tracking. According to the technical scheme, the processing speed of motion detection can be improved, the complexity of calculation hardware is reduced, and meanwhile the integrity of motion detection across image frames is kept. For example, in an image processing environment associated with autonomous or semi-autonomous navigation (e.g., driving, robotic navigation, etc.), there is a need to quickly and accurately process large amounts of image data to maintain a reliable and latest model of the physical environment. Therefore, high-speed and accurate motion detection of input frame data can be provided according to embodiments of the present disclosure.

The invention provides a motor fault detection method and system based on vibration and current double-domain fusion, and relates to the technical field of motor fault detection.The method comprises the steps that an electric vehicle motor serves as a fault traceability root node, hierarchical progressive structured association topology pruning is conducted, and a minimum complete diagnosis topology is output; backtracking motor time sequence fault records to carry out fault association mining, and constructing an association fault probability matrix; probability graph model parameterization is carried out to obtain a fault probability association topology; executing multi-thread cross-modal feature tracking identification, and outputting a real-time motor fault prediction result and K associated fault prediction results; performing Bayesian inverse probability derivation, and outputting a real-time motor fault probability; and binding as a fusion diagnosis conclusion and outputting. According to the invention, the technical problem of inaccurate fault diagnosis caused by the fact that a single signal monitoring method in the prior art cannot accurately identify multiple fault modes when facing complex motor faults is solved.

The invention discloses a method and device for amplifying the sensitivity of an interference type optical fiber sensor, and relates to the technical field of optical fiber sensing, and the method comprises the steps: collecting original broadband light source spectral data, constructing an artificial spectral data set based on the original broadband light source spectral data and a virtual spectral data set, the method comprises the following steps: acquiring measured spectral data of an interference type optical fiber sensor in a to-be-measured environment in real time, selecting reference spectral data according to a preset target magnification factor, subtracting through a spectrograph channel to obtain a vernier effect spectral data set, performing upper envelope fitting operation on each piece of vernier effect spectral data, recording a wavelength value corresponding to a feature tracking point, and calculating the spectral data of the interference type optical fiber sensor according to the wavelength value. And obtaining a predicted value of the physical quantity to be measured based on demodulation of the wavelength value-physical quantity parameter table. According to the virtual spectrum data set generating and processing method, the sensitivity of the interference type optical fiber sensor is remarkably improved, the system structure is simplified, the cost is reduced, and the stability and reliability of the system are improved.

This invention discloses a real-time target tracking method for unmanned aerial vehicles (UAVs) based on asynchronous semantic correction. It employs a spatiotemporally decoupled architecture of a high-frequency geometric main thread and an asynchronous semantic correction thread. The main thread estimates the target's geometric state frame-by-frame using sparse feature tracking and filtering models, outputting a target prediction bounding box. The asynchronous thread calls a lightweight semantic segmentation model at low frequency according to trigger conditions to generate a target semantic mask and uses the motion trajectory within the delay period for spatiotemporal alignment compensation. The system performs dual reliability checks on semantic input and output, updating the target state through a hierarchical write-back mechanism. When the main thread's geometric propagation fails, an emergency fallback tracker takes over the output to ensure tracking continuity. This invention enables category-independent, real-time, robust target tracking in open scenes on edge platforms.

The embodiment of the invention provides a driving scene image generation method and device based on Mama, electronic equipment and a storage medium, and relates to the technical field of image processing. The method comprises the following steps: performing coding quantization processing on an image sequence in an acquired real driving scene to obtain a discrete potential representation sequence, performing optical flow estimation and feature tracking on the image sequence to generate a sparse motion trail diagram of the image sequence, selecting multiple frames of images from the image sequence as key frames, and obtaining a sparse motion trail diagram of the image sequence; extracting guide pixels from each key frame based on the sparse motion trail diagram, inputting the sparse motion trail diagram and the guide pixels into a double-branch conditional encoder to obtain a fused conditional vector, inputting the potential representation sequence and the conditional vector into a Mamba model to generate a predicted potential representation sequence with a target length, and decoding the predicted potential representation sequence to obtain a predicted potential representation sequence with a target length. And obtaining a predicted image sequence. Therefore, the sense of reality and the dynamic continuity of the predicted image sequence can be obviously improved.

Techniques and systems are provided for pose prediction. For instance, a process can include predicting a future pose of the apparatus; identifying a set of tracked features; and selecting a subset of cameras from a plurality of cameras for feature tracking based on the identified set of tracked features and the future pose of the apparatus.

In various examples, techniques for using hardware feature trackers in autonomous or semi-autonomous systems are described. Systems and methods are disclosed that use a processor(s) to determine flow vectors associated with pixel locations in a first image. The systems also use the processor(s) to determine a location of a feature point in a second image based at least on one or more of the flow vectors and a subpixel location of the feature point in the first image. In some examples, the processor(s) may include an optical flow accelerator (OFA) that includes a hardware unit storing a lookup table that is used to determine the location of the feature point in the second image. In some examples, the processor(s) may include an OFA to determine the flow vectors and a vision processor to determine the location of the feature point in the second image.

The application discloses a line feature tracking method and device based on LK optical flow, and belongs to the field of computer vision.The application solves the problem that the existing method cannot guarantee real-time performance due to large operation amount required by interframe line feature extraction and matching.The method of the application first extracts line features from a collected first frame image by using an improved EDlines algorithm; then, line feature tracking is performed on a next frame image based on the line features in the first frame image and the LK optical flow method, and the image pyramid form is adopted during the line feature tracking.The application solves the problem that the traditional method requires large operation amount for descriptor extraction and interframe matching in each frame image, reduces the time consumption of line feature extraction and interframe matching, and guarantees real-time performance.The method of the application can be applied to line feature tracking in images.

A method including capturing at least one image using a plurality of cameras, identifying a plurality of facial features associated with the at least one image, stabilizing a location of at least one facial feature of the plurality of facial features, converting the location of the at least one facial feature to a three-dimensional (3D) location, predicting a future location of the at least one facial feature, and rendering a 3D image, on a flat panel display, using the future location of the at least one facial feature.

The invention relates to the technical field of data labels, and particularly discloses a label setting method and system for unstructured data, and the method comprises the steps: carrying out the multi-dimensional preprocessing of the unstructured data, and obtaining standardized data; performing feature extraction on the standardized data by adopting a hybrid model fused with a feature extraction operator to obtain deep feature information of the data; generating a dynamic label set based on the deep feature information; matching the dynamic label set with the standardized data through a semantic association degree calculation method to obtain a preliminary label; and monitoring the dynamic change of the data and new business requirements by means of a time sequence feature tracking module, and updating and expanding the preliminary label through a label evolutionary algorithm to obtain a final label. According to the method, a multi-dimensional preprocessing technology is adopted, the quality and pertinence of preprocessed data are improved, and a good foundation is laid for subsequent feature extraction and label setting.

This invention relates to direct scale level selection for multi-level feature tracking. A method for mitigating motion blur in a visual-inertial tracking system is described. In one aspect, the method includes: accessing a first image generated by an optical sensor of the visual tracking system; accessing a second image generated by the optical sensor of the visual tracking system, the second image following the first image; determining a first motion blur level of the first image; determining a second motion blur level of the second image; identifying a scale change between the first and second images; determining a first optimal scale level for the first image based on the first motion blur level and the scale change; and determining a second optimal scale level for the second image based on the second motion blur level and the scale change.

The application relates to the field of computer vision and relates to a measurement method for the movement of a marine floating body. The measurement method comprises the following steps: acquiring a video of the movement of the marine floating body on a water body; determining a feature tracking point set corresponding to each frame of image in the video, the feature tracking point set being related to the marine floating body; and determining six-degree-of-freedom movement parameters of the marine floating body according to the feature tracking point set. The application realizes the measurement of three-dimensional complete movement information, i.e. the measurement of six-degree-of-freedom movement parameters, and can improve the robustness and completeness of the measurement of the movement information of the marine floating body.

The invention belongs to the field of geological disaster monitoring and early warning, and discloses a landslide deformation monitoring method based on SAR image offset tracking and multistage filtering, which comprises the following steps: dividing a registered SAR image data set into image pairs, calculating an original offset field of each image pair by using a block phase cross-correlation algorithm, and calculating the original offset field of each image pair; based on correlation threshold and signal-to-noise ratio threshold filtering, median filtering, statistical aggregation, amplitude threshold filtering, vector consistency filtering and density clustering analysis, multi-stage filtering is fused to comprehensively process an original offset field, noise is effectively removed, systematic deviation is corrected, a real and coherent landslide deformation signal is refined, and the landslide deformation accuracy is improved. Therefore, a more accurate and more reliable landslide deformation monitoring solution is provided. The method focuses on image pixel feature tracking and post-processing thereof, integrates multiple technologies to filter noise step by step, can realize landslide identification and long-term dynamic monitoring, improves the accuracy and reliability of landslide monitoring, and provides technical support for landslide disaster prevention and control.

Embodiments are disclosed for automatic display alignment using feature tracking. In some embodiments, a method comprises: capturing, with a first camera of a first display, a first image; capturing, with a second camera of a second display, a second image, where the respective fields of view for the first camera and the second camera are at least partially overlapped; transforming the first head pose into a first reference frame for the first display; transforming the second head pose into a second reference frame for the second display; generating a dual-display environment for the first and second displays based on the first and second head poses in the first and second reference frames, respectively; presenting a first portion of the dual-display environment on the first display; and presenting a second portion of the dual-display environment on the second display.