9results about How to "Improve tracking stability" patented technology

The invention discloses a control method for operation equipment and a related device, and relates to the technical field of operation equipment. The control method comprises the following steps: determining a driving path and a target end point based on environment information collected by operation equipment; determining a first distance between the current position of the operation equipment and the target end point; based on a comparison result between the first distance and a preset threshold value, adopting a first control strategy or a second control strategy to drive the operation equipment to move, wherein under the condition that the first distance is greater than the preset threshold value, path tracking is carried out along the driving path by adopting the first control strategy; and under the condition that the first distance is smaller than or equal to the preset threshold value, switching to the second control strategy to enable the operation equipment to move to the target end point. According to the invention, the operation precision of the unmanned forklift in a complex outdoor scene can be improved.

The invention relates to an airborne end'low-slow-small 'target identification tracking method and system based on improved YoV5 and ECO, belongs to the technical field of unmanned aerial vehicle target identification and tracking, and solves the problems that'low-slow-small' target identification accuracy is low and tracking is easy to lose in the prior art. Adding a small-scale detection head P2 at a detection end to obtain the detection end of the low-slow-small target detection model; adding a feature fusion layer F2 corresponding to the detection head P2 to a neck network of the YoV5 model, and adding a CBAM model to each feature fusion layer of the neck network to obtain the neck network; training a low-slow-small target detection model based on the training set, performing low-slow-small target detection identification on a target image acquired in real time through the trained low-slow-small target detection model, and outputting a target identification result; and determining a tracking target based on the target identification result, and continuously obtaining continuous frame images of the tracking target based on a preset tracking algorithm.

This invention specifically relates to an optical target tracking method based on trajectory guidance and spatiotemporal joint decision-making, belonging to the field of computer vision and video information processing technology. The method first extracts image features through an anchor-free twin network architecture, outputting classification, centrality scores, and regression response maps; then, it constructs a sliding window weighted least squares trajectory prediction model, modeling target motion as locally uniformly accelerated motion to achieve target position prediction; subsequently, it generates an adaptive Gaussian heatmap based on trajectory prediction uncertainty, constructing a spatial attention mechanism for trajectory guidance; finally, it fuses visual features and prior trajectory information through a linear weighted fusion strategy to output the target position. This invention effectively utilizes temporal motion information, solving the problems of low tracking accuracy and poor robustness in existing technologies under occlusion and fast-moving scenarios, balancing real-time performance with low computational overhead, and is suitable for ordinary optical and infrared thermal imaging tracking scenarios.

The utility model relates to stainless steel annealing technical field, and disclose a kind of stainless steel annealing furnace tail rectification device, comprising: rectification box and processing furnace, the rectification box is fixedly connected at the top of processing furnace, the first trapezoidal block is slidably connected in the inner chamber bottom of rectification box, the side fixed connection of first trapezoidal block has L-shaped movable plate, the end fixed connection of L-shaped movable plate has rectification plate, the inner chamber of processing furnace is slidably arranged in rectification plate, the inner chamber upper end of rectification box is slidably connected with second trapezoidal block, the first trapezoidal block is provided with the sliding member for connecting with the inclined plane of second trapezoidal block, the side wall of rectification box is set to inclined plane, the sliding member includes sliding slot, sliding rod and sliding block, the sliding slot is opened in the inclined side wall of first trapezoidal block;The new scheme can prevent deviation, improve rectification stability, so as to not need to carry out cooling treatment to steel band, adjust the deviation problem on line, improve overall production efficiency.

The application discloses a kind of near distance newly generated vehicle target positioning tracking method and system based on millimeter wave radar, it is related to vehicle target tracking technical field, to solve the problem that the initial state estimation of new target is not accurate in the existing target tracking method, leading to tracking failure.The technical points of the present application include: clustering the preprocessed point cloud data, obtaining dynamic target point cloud cluster;Using right angle corner detection method or long side directionality detection method to extract features from dynamic target point cloud cluster to obtain the initial state of dynamic target;The initial state of dynamic target is input into the target tracking algorithm to continuously track the dynamic target and predict its motion state.The initial state of the new target obtained by the present application has higher precision, and the subsequent tracking filter can converge faster and stabilize, thereby improving the spatial position accuracy, speed accuracy and tracking stability of target tracking.

The application discloses a multi-target tracking method based on Gaussian mixture probability hypothesis density filtering, and belongs to the technical field of multi-target tracking. In order to solve the problems of target missing tracking and track false deletion in the high-density and close multi-target scene, the application obtains the measurement data corresponding to each sensor, and assigns the measurement identifier corresponding to the measurement data; the Gaussian component of each sensor is updated, and the measurement source label of the Gaussian component is set as the measurement identifier corresponding to the sensor measurement data; an adaptive pruning and merging decision model is established; all Gaussian components are input into the adaptive pruning and merging decision model for merging or pruning processing to obtain the remaining Gaussian components; the state extraction weight threshold is set, the remaining Gaussian components are compared, and the mean value of the Gaussian component with the weight higher than the state extraction weight threshold is output as the final extracted target state. The application realizes the significant improvement of the multi-target tracking precision, robustness and continuity.

The invention relates to the technical field of automotive electronics, in particular to a point cloud pedestrian recognition and tracking method based on speed fluctuation characteristics, a sensing system and a vehicle, and the method comprises the following specific steps: S1, data acquisition and preprocessing; s2, estimating the overall movement speed of the target; s3, speed fluctuation feature extraction; s4, target classification based on multiple features; s5, an improved tracking management strategy; s6, performing tracking management on the classified targets; the sensing system comprises a memory and a processor, computer programs are stored in the memory, and the processor executes the computer programs and instructions of the computer programs; the vehicle is specifically a vehicle carrying the sensing system. A'micro-motion target 'is used as an independent tracking object, so that the problem that a tracking trajectory is lost and then restarted due to the fact that pedestrians are momentarily static is effectively avoided, a smoother and more continuous trajectory is formed, and more reliable information is provided for a downstream prediction and decision-making module.

This invention belongs to the field of computer vision and artificial intelligence technology, and relates to a multi-target tracking and segmentation method and system based on feature fusion and spatiotemporal memory. The multi-target tracking and segmentation method includes: acquiring the original state features and predicted bounding boxes of each target in each frame of the image, and constructing a spatiotemporal memory database containing a short-term buffer and a long-term storage area; for the first detected target in the multi-target video sequence, instantiating an independent long-term trajectory prototype for it, and establishing a corresponding index in the spatiotemporal memory database; performing hierarchical matching of the original state features and predicted bounding boxes of each target in the frame of the image based on the spatiotemporal memory database; updating the Kalman filter parameters and the spatiotemporal memory database based on the matching results; and continuing the tracking and segmentation of the next frame of the image using the updated Kalman filter and spatiotemporal memory database until each frame of the image has been traversed to obtain the multi-target tracking and segmentation result. This invention can effectively solve the problem of re-identification after long-term target occlusion.

The application discloses a kind of external key point matching and tracking trajectory calculation method and device based on VGGT.The method comprises: loading configuration file and initializing model;Load external key point coordinates and carry out standardization and quantity alignment;First, input image is converted into tensor format, then image features are extracted, and finally L2 normalization is carried out;Depth feature vector of external key point is extracted from feature map;Calculate cosine similarity to evaluate matching quality;The average amplitude and standard deviation of feature vector are calculated to evaluate feature quality, and the matching threshold is dynamically adjusted;The coordinate difference of matching point is calculated to eliminate the matching that does not meet geometric constraint, and tracking trajectory is generated;Tracking quality analysis and visualization are carried out.The matching success rate of the application is greatly improved, the false matching rate is obviously reduced, the time consumption of processing image is obviously shortened, the efficiency is also greatly improved, the tracking stability is obviously improved under dynamic interference, and automatic high-precision inspection without manual intervention is realized.