| dc.description.abstract | Object tracking aims to detect the position of a target in a continuous image sequence,predict its movement trajectory, and update the position of the target frame by frame.To accomplish this task, two features are currently used to associate the same target between different frames. One is to obtain the current position of the target through theappearance characteristics of the target by matching the pixel features in the detectionframe of the previous and next frames. The second is to use the motion characteristicsof the target and use dynamic system models such as Kalman filter to use the historyof the target. position to predict the current position of the target. After obtaining thetwo features, the two features are used according to different situations, using differentweight ratios to calculate the distance cost between the prediction frame and the detectionframe. Finally, through the Hungarian algorithm, The lowest-cost prediction frame anddetection frame are matched and assigned corresponding IDs to complete tracking.In the case of a dynamic camera, if the motion features are predicted only through thetraditional Kalman filter, it may lead to bias. The reason is that when the camera moves,the pixels of the image will be shifted in the opposite direction according to the movingdirection of the camera. This offset cannot be estimated by the Kalman filter by itself,which will cause the predicted target position to deviate and cause the target to be lost.In order to solve this problem, in 2022, the BoT-SORT model dealt with this problemthrough CMC camera motion compensation (Camera Motion Compensation). They usedsparse optical flow to predict the movement of feature points between frames, and thencalculated the camera Errors caused by movement, and a set of methods are proposedto use the calculated errors to correct the predictions of the Kalman filter. This methodsignificantly improves the tracking performance under dynamic cameras. However, thereis still room for improvement. The amount of calculation caused by the sparse opticalflow used to calculate the deviation cannot be ignored, and since prediction is based onthe feature points of the image, the quality of the image is limited to a certain extent.The degree also affects the accuracy of prediction.In this article, we propose a method to calculate the camera movement error throughcamera parameters and correct the Kalman filter through the camera movement compensation mechanism. This method converts the camera movement in the 3D space throughthe principle of the pinhole camera model. The image deviation from the 2D space notonly reduces the huge amount of calculation caused by sparse optical flow, but also requires low calculation cost and avoids relying on the feature points of the image. At thesame time, we were inspired by papers related to target tracking and related In this stage,the prediction frame and the detection frame are expanded according to the position between the targets to reduce tracking loss due to small errors, which allows our trackingmodel to achieve faster and more stable performance. | en_US |