130 results about "Multi target tracking" patented technology

The application discloses a kind of based on multi-objective optimization algorithm's advertisement putting real-time effect tracking method, advertisement putting data analysis technical field, including step one: obtaining advertisement putting real-time behavior data;Step two: constructing advertisement putting effect window matrix;Step three: through improved MiniRocket network, execute advertisement effect conduction difference feature extraction and continuous response fragment aggregation processing;Step four: constructing multi-objective tracking objective function set and multi-objective optimization constraint condition;Step five: using improved MOEA / D algorithm, execute state traction decomposition optimization and fragment duration constraint neighborhood replacement processing;Step six: by Canberra distance, execute target deviation analysis processing;Step seven: match target advertisement plan's real-time effect tracking state.The application improves the accuracy of advertisement putting real-time effect tracking by improved MiniRocket network and improved MOEA / D algorithm.

The invention discloses an automatic focusing method and device, computer equipment and a computer readable storage medium, and relates to the technical field of focusing. The method comprises the following steps: performing multi-target tracking detection on a preview image frame to obtain a face frame set, a human eye frame set and a multi-target tracking parameter; performing scale measurement aperture unification on the face frame set and the eye frame set to obtain a target face frame width and a target eye frame width; calculating a human face available threshold value and a human eye available threshold value; determining a depth-of-field coverage judgment result of the focusing target according to a depth map corresponding to the preview image frame; and according to the target face frame width, the target eye frame width, the multi-target tracking parameter after smoothing normalization, the depth-of-field coverage judgment result, the face available threshold and the eye available threshold, determining a focusing strategy to perform focusing switching. Therefore, the stability of the exported picture, the accurate focus and the smooth switching are ensured through automatic focusing.

A multi-target tracking and monitoring method and system with high trajectory consistency, belonging to the field of computer vision, solves the problems of identity switching and trajectory fragmentation caused by long-term target occlusion and dense interaction in multi-target tracking, especially in UAV monitoring scenarios. The method includes the following steps: 1. Inputting a video sequence, performing target detection and feature acquisition for each frame; 2. Performing preliminary association through a basic tracker to obtain an initial trajectory; 3. Detecting the disappearance and appearance of abnormal trajectories using a position awareness submodule; 4. Storing abnormal trajectory features using a long-term memory submodule; 5. Performing secondary association using a multi-step cross-frame matching submodule; 6. Completing trajectory updates. This invention is applicable to UAV monitoring scenarios such as high-altitude inspection, low-altitude operations, and outdoor scene monitoring.

This invention provides a method for the evolution of a multi-target tracking network based on spatial topology and identity aggregation. The method includes: (1) training a multi-target tracking network and an identity aggregation network based on spatial topology in a labeled source domain to obtain parameter-updated multi-target tracking and identity aggregation networks; (2) using the parameter-updated multi-target tracking and identity aggregation networks to obtain pseudo-labels for data in an unlabeled target domain; and (3) using the obtained pseudo-labels and the self-supervised module in the multi-target tracking network to update the parameters of the entire multi-target tracking network. The network parameters obtained after iteratively executing steps (2) and (3) for multiple rounds are used for multi-target tracking in the target domain. This invention can solve the domain adaptation problem in multi-target tracking and suppress the significant performance degradation of multi-target tracking network models in unlabeled tracking scenarios.

Multiple Object Tracking (MOT) procedures are used to analyze a video stream to identify and track objects and events of interest across frames in the video stream. According to various embodiments, two or more different models may be separately applied to track an object across multiple video frames. A model may be dynamically evaluated for a frame or group of frames by determining a performance metric for the model, for instance on the level of a frame or group of frames. Then, two or more models may be fused together using a weighting scheme based at least in part on performance metrics for the different models. The fused model may be used to track objects across the frames.

This invention discloses a method, system, and equipment for identifying the construction progress of roller-compacted concrete dam surfaces: It acquires multi-camera video of the roller-compacted concrete dam surface, performs time synchronization, camera calibration, coordinate unification, regional modeling, and rasterization, and preprocesses the surface video; it detects, identifies, segments, and tracks construction equipment targets and surface conditions, mapping machine trajectories and work footprints to the construction plane, thereby completing statistical analysis of coverage area, number of compaction passes, dwell time, compliance status, and abnormal events; it calculates construction progress and efficiency indicators, compares them with the plan, and outputs progress diagnosis results, early warning information, scheduling suggestions, and report interface data. This invention utilizes on-site monitoring video, improving robustness under complex lighting, occlusion, and dust conditions through multi-camera spatiotemporal registration and deduplication fusion, reducing manual inspection costs, and enhancing the objectivity, timeliness, and traceability of progress perception.

The application discloses a Kalman filtering state decoupling and parallel computing method for multi-target tracking, and comprises the following steps: S1, generating several two-dimensional Kalman subsystems according to the original high-dimensional state space and column vectors of Kalman filtering hardware; S2, constructing two-dimensional Kalman filtering processing units for each two-dimensional Kalman subsystem, and outputting a high-dimensional state vector; and S3, generating a high-dimensional target state estimation result according to the high-dimensional state vector. When the multi-target tracking algorithm is applied, the parallel two-dimensional Kalman filtering processing array is simultaneously executed in the FPGA, and the processing delay of single filtering is greatly shortened. The soft and hard collaborative architecture completely sinks the heavy matrix operation to the FPGA, significantly reduces the CPU load, enables the CPU to focus on upper logic control, and thus realizes real-time multi-target tracking with high frame rate and low delay on the end-side heterogeneous platform.

Embodiments of the present application disclose a cross-camera multi-target tracking method, device, equipment and medium, which comprises the following steps: acquiring images of a current moment collected by a plurality of preset cameras; determining the targets, positions and target features of the targets appearing in each image by using a neural network model; determining a target as a non-stable target if the number of times of appearance of the target is less than a preset first threshold value; and determining a first stable target matched with each non-stable target by using a Hungarian algorithm, i.e. performing global optimal matching, so as to ensure that the same target is still accurately identified when crossing different camera collection areas, and further ensure the uniqueness of the target identity.

This invention discloses a multi-target tracking method for mining areas based on multi-scale attention and graph neural networks, belonging to the field of environmental perception for unmanned driving in mining areas. The method includes: multimodal perception and feature enhancement, employing a multi-scale attention mechanism for spatial, channel, and multi-scale adaptive weighting; target detection and re-identification feature extraction; temporal-spatial joint modeling, constructing a cross-frame target spatiotemporal map, and obtaining a spatiotemporal correlation probability matrix through graph neural network inference; obtaining a comprehensive correlation cost matrix based on mining area scene knowledge constraints, and using a knowledge base including static geography, dynamic operations, and equipment characteristics for hard constraint filtering and soft constraint optimization; trajectory lifecycle management, outputting a stable trajectory. This invention enhances feature robustness through multi-scale attention, achieves global spatiotemporal correlation through graph neural networks, and improves decision rationality through scene knowledge constraints, effectively solving the problem of target loss due to occlusion in complex mining environments.

The utility model provides a kind of conformal phased array antenna and system, conformal phased array antenna includes: conformal carrier, set on vehicle body structure, the structure of the conformal carrier is matched with the vehicle body structure;Antenna array, including multiple antenna units distributed in array, set on the first surface of the conformal carrier;Chip array, including multiple radio frequency chips distributed in array, the radio frequency chip is set on the second surface of the conformal carrier, is connected with the antenna unit, through with vehicle body structure matched conformal carrier on setting antenna unit and chip array, it can increase the coverage range of radar antenna beam, significantly improve the multi-target tracking performance of radar under complex traffic environment, improve the security of intelligent driving.

This invention discloses a detection-enhanced multi-target vehicle tracking method based on feature repair and relationship optimization. Video frames are input into a backbone network to extract shared features, constructing degraded observation features and reference features. The reference features are then subjected to mask-guided forward diffusion, combined with degraded observation features for reverse denoising to obtain repaired features. After uncertainty evaluation, these repaired features are adaptively fused with the degraded observation features to obtain fused features. The fused features are input into a detection decoder to generate a detection embedding. This embedding is then combined with historical tracking embeddings to model local competition relationships, resulting in an optimized detection embedding. The optimized detection embedding and historical tracking embeddings are input into a joint decoder, and the trajectory is updated by a trajectory management module. The tracking result is output, and the tracking embedding for the next time step is generated. The model is trained using low-light and partially occluded datasets, and the results are visualized. This invention's method exhibits strong detection discriminative power and tracking stability in multi-target vehicle tracking under low-light and partially occluded environments.

This invention proposes a method for multi-target recognition, tracking, and vibration extraction of bridge cable-stayed bridge videos based on unmanned aerial vehicles (UAVs). The method includes: Step 1: Constructing a refined tilted and slender target detection model for bridge cable-stayed bridges based on the YOLOv11 model; Step 2: Proposing a multi-target tracking algorithm that integrates the tilted and slender target detection model and the StrongSORT algorithm; Step 3: Improving the displacement extraction method by combining SIFT / ORB feature point matching and sub-pixel refinement techniques; Step 4: Designing a UAV motion correction algorithm based on variational mode decomposition and time-frequency domain joint screening; Step 5: Constructing a joint working mode analysis algorithm that combines natural excitation technology and random subspace recognition algorithm. This method achieves high-precision extraction of cable-stayed bridge vibration signals and identification of cable-stayed bridge modal parameters, providing technical support for health monitoring of long-span cable-stayed bridges.

The application discloses a kind of non-rigid space mapping-based multi-camera multi-target tracking methods without calibration, it is related to computer vision and intelligent video analysis technical field.The method includes: obtaining the position of target bounding box in each camera and the corresponding appearance feature representation;Extract the effective area that can be used for geometric modeling, generate binary effective area mask;Get dense displacement field and spatial correspondence confidence;Fixed step is constructed on the source camera image Sampling grid, the grid point is screened, and the high-confidence control point set from source view to target view is formed;According to the control point set, establish the cross-camera space mapping relationship under non-calibration condition;The spatial geometric similarity of target bounding box in different cameras is calculated, and the cross-camera target association is carried out by combining the appearance feature similarity, to realize the multi-camera multi-target tracking under non-calibration condition.The method can establish the cross-camera space mapping relationship, realize stable multi-camera multi-target tracking.

The present application relates to a kind of underwater multi-target tracking methods, comprising the following steps: establishing three-dimensional space coordinate system, obtaining the coordinate of sensor, monitoring water volume, water surface in the coordinate Z2 of Z axis, the coordinate Z3 of water bottom in the Z axis, clutter density λ;Sensor periodically obtains measurement information, the measurement information includes azimuth, elevation and the time delay between direct wave, the set of measurement information in the i frame is recorded as Z (i), the number of measurement information that sensor obtains single target is L;Randomly generate K in monitoring water volume Virtual target c K =[c1,c2,...,c K ] In prior formula, the possibility that a measurement information comes from certain propagation path is expressed;According to log likelihood ratio, establish underwater target model;According to underwater target model, the actual target number and the optimal coordinate of each actual target are obtained. Efficiently positioning and tracking underwater multi-target can be carried out.

This invention discloses an underwater multi-target tracking method based on stable semantic regions, relating to the fields of computer vision and underwater intelligent sensing technology. The method includes: acquiring underwater images and defining stable semantic regions for fish heads; performing feature extraction and fish head detection on the underwater images to obtain fish head detection results; generating a segmentation mask for the stable semantic region of each fish head based on the fish head detection results within the multi-target tracking framework; calculating the physical centroid of the stable semantic region of the fish head based on the segmentation mask, using the physical centroid as a tracking anchor point for target association and trajectory tracking; determining the real-time spatial position of the fish body and its real-time attitude by using the physical centroid of the stable semantic region of the fish head as a reference point and combining it with the positional information of the stable semantic region of the fish head, thus completing multi-target tracking. This invention effectively avoids problems such as positioning deviation and missed detection caused by the non-rigid deformation of the fish body, improving the accuracy and stability of underwater fish swarm tracking.

This application relates to the field of gimbal technology, and more particularly to a gimbal multi-target tracking method, gimbal, and medium for live streaming scenarios. The method involves acquiring the first frame image from a live streaming camera, detecting all detected targets and constructing a multi-target identity queue. The image is then input into a multi-target tracking model, which outputs predicted bounding boxes and tracking confidence scores for each target. Based on the confidence scores, the target loss status is determined. A recapture operation is performed on the lost target; if recapture is successful, tracking resumes and parameters are updated; if recapture fails and times out, the target is removed from the queue. This process filters to obtain a set of trackable targets. The gimbal aiming point is determined by combining the current scene type and the predicted bounding boxes of each target. Control commands are generated based on the deviation between the aiming point and the image center to drive the gimbal rotation and calibration. This application enables stable and continuous multi-target tracking in live streaming scenarios, effectively reducing target loss and improving the stability of live streaming footage and viewing experience.

The application discloses a kind of based on autoregressive motion model's occluded target tracking method, system and equipment, it is related to multi-target tracking technical field, based on autoregressive motion model's occluded target tracking method by trained context information extraction network obtains the neighbor context information of occluded target, then the neighbor context information and the historical motion track of occluded target itself are input into motion prediction network to obtain predicted trajectory, finally, predicted trajectory is associated with remaining detection frame using KM algorithm, long time multi-target occlusion problem can be effectively coped with, and the efficiency and accuracy of multi-target tracking are improved.

This invention relates to the field of computer vision technology and discloses a multi-target tracking method and apparatus. The method includes: acquiring a video frame sequence; performing target detection on each frame in the video frame sequence to obtain a target detection box sequence; and extracting the ReID feature vector corresponding to each target detection box sequence; constructing a spatiotemporally adaptive threshold surface based on the target detection box sequence; forming an initial trajectory group based on the target detection box sequence and the corresponding ReID feature vector; performing iterative graph clustering and merging on the initial trajectory group based on the spatiotemporally adaptive threshold surface to obtain an iterative trajectory group, and using the final trajectory group as the target tracking result. This invention achieves dynamic adaptive adjustment of similarity requirements based on the spatiotemporal distance between targets by constructing a spatiotemporally adaptive threshold surface instead of a fixed threshold; furthermore, it employs an iterative graph clustering and merging strategy to globally optimize and progressively merge trajectory segments, significantly enhancing the continuity and integrity of the trajectory.

This invention belongs to the technical field of water quality monitoring, and specifically relates to a method, apparatus, equipment, medium, and system for determining water quality early warning levels. The invention provides a method for determining water quality early warning levels, comprising: acquiring real-time images of a fish population in a target aquarium, wherein the target aquarium is connected to a target water area, and water from the target water area circulates in the target aquarium before entering the target water area; determining individual behavioral characteristics and school behavior characteristics of each fish within a preset time period based on the real-time images; determining the current state of the fish population in the target aquarium based on the individual and school behavior characteristics; and assigning a water quality early warning level to the target water area based on the current state. This method achieves multi-target tracking and quantitative analysis of behavioral characteristics, improving the timeliness and accuracy of monitoring, and providing technical support for rapid response to water pollution incidents.

A system for generating a real-time object-focused video using minimal camera arrays with pre-computed sports-field-optimized spatial mapping. The system positions virtual cameras to maintain tracked objects in focused, straight-ahead orientations while supporting one-dimensional movement between two cameras using geometric interpolation and two-dimensional movement within three-camera triangular configurations using barycentric coordinates. Computer spatial mapping with discretized depth information optimized for fast-moving object tracking in sports environments eliminates real-time depth calculation overhead, avoiding latency bottleneck and enabling ultra-low latency processing suitable for live sports broadcasting. The system includes predictive camera set switching using mathematical positioning variables, multi-object tracking capabilities with distributed processing frameworks, and intelligent 2D occlusion handling optimized for broadcast video output with parallax-induced occlusion management. Video synthesis techniques including adaptive geometric transformation, and multi-resolution image processing achieve rapid processing performance for live broadcasting applications while preventing discrete camera switching artifacts through continuous interpolation coefficients that eliminate abrupt perspective transitions.

The application discloses a multi-target tracking method based on ROI and a homologous matrix, adopts a binocular camera to acquire video images of a to-be-tracked target in real time, matches each candidate circular marker in the first frame images of left and right videos, takes the successfully matched candidate circular marker as an effective tracking target, allocates an identity ID to the effective tracking target one by one, and acquires a corresponding region of interest ROI; according to each effective tracking target in the last frame image, the corresponding region of interest ROI of the current frame image is intercepted, candidate circular markers in each region of interest ROI are detected again, if there are candidate circular markers, the candidate circular markers are updated as current candidate circular markers, and the corresponding identity ID is inherited; if there are no candidate circular markers, current candidate circular markers are estimated, a displacement amount of the candidate circular markers corresponding to the last frame image before being blocked is calculated, short-time blocking or long-time blocking processing is triggered, and three-dimensional coordinates and a displacement amount of the inherited identity ID are calculated.

The present application relates to a kind of passive multi-band sensor array system and multi-target tracking method, wherein the system includes: multi-band sensor group, ambient illuminance sensor, synchronization and control unit and preprocessing unit;Multi-band sensor group includes visible light, near infrared and long-wave infrared sensor integrated in unified shell by coaxial light path and all do not include active emitter;Synchronization and control unit generates synchronization signal, controls multi-band sensor group and ambient illuminance sensor to carry out synchronous data acquisition;Preprocessing unit receives raw image data, ambient illuminance data and synchronization signal, outputs structured multi-modal data package, provides for the multi-target tracking engine of back end, and the tracking state of multiple targets is output by multi-target tracking engine.The present application can realize stable, continuous, high-precision tracking to multiple targets in a variety of complex scenes, effectively solve the problem that existing multi-target tracking scheme environment adaptability is poor, depends on active emission, robustness is insufficient.

The application discloses an air target tracking evaluation method based on infrared search, relates to the technical field of target tracking, and comprises the following steps: collecting an infrared image sequence in real time and eliminating background images, converting a target enhanced image into a binary image, randomly determining a pixel point with a value of 1, selecting eight neighbor pixels adjacent to the pixel point, connecting the pixel points with the same value of 1, and forming a connected domain; acquiring all the connected domains, calculating the characteristic parameters of each connected domain for screening, marking the screened connected domains as candidate targets, assigning an exclusive ID to each candidate target, forming a candidate target list, performing background elimination and adaptive threshold segmentation on the collected infrared image sequence, extracting the candidate targets, and constructing a tracking chain containing appearance and motion features, so that continuous and stable tracking of the targets is realized. In view of the problem that identities are easily confused in multi-target tracking, a graph neural network is introduced to globally optimize identity distribution of target nodes with associated ambiguity, and identity switching is effectively inhibited.

The application belongs to the field of radar resource management, and provides a real-time optimization selection method for sky-wave and ground-wave mixed propagation paths for target tracking. The application takes the sum of Cramer-Rao lower bounds (PCRLB) of multi-target tracking as a performance index. At each time, the contribution degree of each combined path to the measurement information in the objective function is evaluated, and on the basis of meeting the constraint condition, the corresponding receiving and transmitting combined path is selected in turn according to the contribution degree from large to small, so as to obtain the optimal path selection strategy at the moment. The application can realize real-time sky-wave and ground-wave mixed path selection, and adaptively adjust the path selection relationship according to different scenes, so as to realize reasonable allocation of limited receiving and transmitting node resources.

This invention discloses a prediction residual-driven multi-target tracking method, belonging to the field of computer vision technology. First, target detection is performed on the current frame. Based on the updated model transition matrix and model probabilities from the previous frame, multiple motion models are used to predict existing target trajectories in parallel within a unified state space, and the prediction results are weighted and fused to obtain the target prediction state. Motion constraints are constructed using the motion uncertainty index from the previous frame, and together with geometric overlap and motion direction consistency, they form an association cost, realizing data association between the trajectory prediction state and the detection boxes. Kalman filtering is performed on the successfully associated detection boxes to update the data, and the prediction residual is calculated. Based on the prediction residual, the model transition matrix is ​​adaptively adjusted, and an equivalent prediction residual is constructed to quantify the target motion uncertainty index for data association in subsequent frames. This method improves the robustness and stability of multi-target tracking in complex scenes without relying on appearance features.