Long-time target tracking method based on depth detection

Abstract

The invention discloses a long-time target tracking method based on depth detection, and belongs to the field of pattern recognition and intelligent information processing. According to the method, anMDNet depth detection tracking framework is adopted, and the problem of imbalance of positive and negative samples during sampling is solved by improving a shrinkage loss function on the basis of difficult-to-separate sample mining; designing and maintaining a high-confidence reserved sample pool during online tracking, reserving a first-frame target and high-confidence result sample characteristics, and performing online training by utilizing the reserved sample pool to update model parameters; and finally, calculating confidence coefficients of candidate samples obtained by Gaussian sampling around the target position of the previous frame through the model so as to track the position of the moving target and maintain the robustness of the model through effective updating. According tothe method, excellent tracking precision and success rate are kept in a complex long-term tracking environment, the target position can be accurately positioned when the target is shielded and reappears after the view, and the design requirement of an actual engineering system is met.

Description

technical field [0001] The invention relates to a long-term target tracking method based on depth detection, which belongs to the field of pattern recognition and intelligent information processing. Background technique [0002] With the development of the field of computer vision, object tracking has received more and more attention and applications in the fields of human-computer interaction, video surveillance, automatic driving and robotics. Common early tracking models include particle filter, mean shift, correlation filter and their derivative models. Although the accuracy and speed of these traditional trackers have been significantly improved in recent years, the feature extraction of the target is still based on shallow features such as optical flow, appearance shape, and color, and cannot capture the semantic features of the target. It is difficult to maintain robust tracking in the face of long-term tracking when the target's appearance is deformed, occluded, or ...

AI Technical Summary

Problems solved by technology

[0007] In order to solve at least one of the above problems, the present invention proposes a long-term object tracking based on deep detection (LT-MDNet) method to solve the problem of occlusion and target out of view during long-term tracking , which can further improve the class imbalance problem during sampling, and enable the model to be effectively updated during online tracking to adapt to changes in the tracking environment, improve long-term target tracking performance, and meet the design requirements of actual engineering systems

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