53results about How to "Accurate target tracking" patented technology

The embodiment of the application discloses a target tracking method, device, equipment and medium. The method comprises the following steps of acquiring a first video stream and performing the following data processing operations on each video frame in the first video stream of firstly detecting a candidate region in the current video frame according to the target detection model and the currentvideo frame; then according to the feature extraction model and the candidate region, extracting the depth features corresponding to each candidate region; then according to the depth feature corresponding to each candidate region and the depth feature of the target tracked in the previous video frame, calculating the feature similarity corresponding to each candidate region; finally determining the target tracked in the current video frame based on the feature similarity of each candidate region. By using the object detection model to detect the object in the current video frame range and tracking the object based on the depth feature, the method can effectively prevent the tracking drift or tracking object lost, and ensures the accuracy of target tracking.

The invention discloses a target tracking method based on supervised significance detection. The target tracking method comprises steps that a searching area of a current frame is divided into super pixels, and super pixel characteristics of a target and a background are extracted, and a support vector machine SVM is used to learn the discriminant appearance model; each time when a new frame of image occurs, the super pixel segmentation of the searching area is carried out, and first-stage significance detection is carried out by using manifold sequencing based on a graph model; the probability of every super pixel of the new frame of image belonging to the target is calculated according to the discriminant appearance model, and classification results are adjusted, and by combining with the first-stage significance detection, a classification result is adjusted, and random walk seed points are selected by combining with the first-stage significance detection, and a second-stage saliency map is acquired by adopting random walk; by adopting the weighting of the saliency map and the classification result, a confidence graph is acquired, and by processing the confidence graph, an integral image is used to estimate the new position and the new dimension of the target. Problems such as rapid motion and deformation are effectively processed, and therefore robustness tracking is realized.

The invention provides a target tracking method and device. The method includes the steps of: collecting a depth image and a color image of a current video image; obtaining at least two target regions in the depth image according to the depth image; performing face detection on the color image corresponding to each target region, and obtaining a face region corresponding to each target region; and when the distance between a first face region corresponding to a first target region and a second face region corresponding to a second target region is smaller than a preset threshold value, using a coring coherent filter to track the first face region and the second face region. The target tracking method realizes accurate tracking of multiple targets when there is the target shadow interference in the multiple target tracking and the shielding interference during target interaction.

The present invention discloses a multi-repetition frequency radar target tracking-before-detection method adopting an extended table auxiliary method to resolve ambiguity and belongs to the radar target detection and tracking technical field. According to the method, a multi-repetition frequency auxiliary extended table considering measurement and quantization errors is established so as to be adopted to perform ambiguity resolution on multi-repetition frequency points; the clutter and noise suppression capacity of a tracking-before-detection method is adopted to effectively suppress ambiguity resolution-post false points; and the real flight track of a target can be restored ultimately. With the method of the invention adopted, the technical problem of measurement information mismatch of a traditional ambiguity resolution algorithm due to the restrictions of remainder theorem application conditions and the technical problem of the incapability of the tracking-before-detection method to correctly accumulate the energy of a target under an ambiguous space can be solved, the detection and tracking of the target under a multi-repetition frequency radar system can be realized. The extended table auxiliary method adopted in the invention can greatly reduce the amount of computation required by ambiguity resolution.

The invention discloses an adaptive mean shift algorithm based on local invariant feature detection. According to the adaptive mean shift algorithm based on local invariant feature detection, local invariant feature detection and the adaptive mean shift algorithm are combined, the detection and matching of local invariant feature points of an object are introduced during searching, and the region of search is recalculated through obtained matched feature points, so that the region of search can be excellently constrained around a target range, and finally, the accuracy of a tracking process is ensured. The adaptive mean shift algorithm based on local invariant feature detection has the advantage that the accuracy and stability of searching are greatly improved relative to those of adaptive mean shift algorithms.

The invention discloses a real-time color object tracking method. The method comprises the following steps: selecting a target template under the particle filtering frame, and initializing a particleset; obtaining a current frame, and obtaining a new particle set through a state transfer model; extracting the target color information and movement information, carrying out the drifting of each particle of the new particle set through an improved mean value drift algorithm, and obtaining a particle set after drifting; building a new observation model based on the fusion of the target color information and the movement information, and calculating the particle weight of the particle seat after drifting; carrying out the normalization of the particle weight, and obtaining the state of the current frame through the weighing of the particles. The method provided by the invention can iron out the defect that the particle filtering calculation burden is large, can inhibit the background interference, and achieves the stable and real-time tracking of a target.

The invention provides a TCAS system S-mode inquiry sequence arranging method. First, effective targets are sorted according to the strength of received intermittent oscillation response signals, the priority tracking and detection inquiry order is determined, and detection inquiry signals are sent first for the effective targets with higher strength of intermittent oscillation response signals. Then, detection inquiry signals are sent to determine the relative height of a current aircraft relative to each effective target in the air and the absolute height of the aircraft, the potential threat degree of each effective target in the air is calculated according to the relative heights and the absolute height, and monitoring inquiry signals are sent first for monitoring inquiry for the effective targets of higher threat degree. Compared with the prior art, inquiry resources can be fully utilized, S-mode target tracking is more accurate, and the stability of target tracking is improved; and the inquiry number, inquiry direction and inquiry antenna of S mode are reasonably arranged by dynamically referring to the response condition of S mode, waste of inquiry resources is further prevented, and the stability of target tracking is improved.

The invention discloses an unmanned ship sea area target tracking method based on a TLD algorithm. According to the tracking method, a TLD algorithm framework is used as the basis of a target trackingalgorithm, the information of a target is executed and maintained through a detection module, a tracking module and a learning module, and an online template is stored, so that the repositioning function of the unmanned ship under the condition that the target in a sea area is lost is realized. The target candidate box generation algorithm uses a Selective Search algorithm based on region namingto generate candidate boxes with fewer numbers and more accurate proportions; the tracking module uses a tracking method based on correlation filtering, so that the tracking effect is improved, and the tracking speed is ensured. The accuracy and robustness of the tracking system are effectively improved under the condition that the illumination and the shape of the sea area target of the unmannedship change greatly.

The invention discloses a target tracking method based on a structured output correlation filter. The method mainly solves the problem that the tracking is failed due to the change, the shading, the rotation and the like of the target illumination in the prior art. The method comprises the following steps of (1) preprocessing a first frame image; (2) constructing a structured output correlation filter; (3) figuring out an optimal structured output correlation filter; (4) preprocessing a current frame image; (5) determining the position of a to-be-tracked target in the current frame image; (6)optimizing the structured output correlation filter; (7) judging whether all frame images in a to-be-tracked video image sequence are selected or not; if yes, ending the process; otherwise, executingthe step (4). According to the invention, the information contained in a sample can be better described by constructing the structured output correlation filter. As a result, characteristics with highdistinguishing degree can be learned through the structured output correlation filter, and a target can be tracked stably and accurately.

The invention relates to the technical field of video tracking, and particularly discloses a video target tracking method and device, electronic equipment and a storage medium, and the method comprises the following steps: obtaining video information; setting initial tracking target information according to the video information; acquiring scale change information of the video information according to the initial tracking target information on the basis of a tracking algorithm which has a scale change tracking characteristic and performs tracking by using an L-K optical flow method under the condition of not performing target position tracking; respectively adjusting the position and the scale of the initial tracking target information according to the position change information and the scale change information so as to track the target in the video information; according to the method, accurate tracking including scale and position parameters can be rapidly carried out on the target in the video information.