Multi-vision tracking and collaboration based on spatial particle filter

Abstract

In existing multi-vision tracking methods, a distributed collaborative tracking mode based on homography constraints is often adopted, yet there are significant shortcomings to this approach. For example, visual information complementation is not used to improve the robustness of tracking, and collaborative tracking is limited by homography constraints. In this study, a three-dimensional spatial particle filter tracking method was proposed, and multi-vision joint tracking and collaboration were effectively achieved. This method was based on the existing particle filter framework. A two-dimensional plane particle was taken as the projection of a three-dimensional spatial particle on the imaging plane, and the formula for calculating a spatial particle’s weight was derived based on Bayesian posterior probability recursion. In addition, an approximation method to determine spatial particle weight was given. The resampling of spatial particles was performed by using an epipolar line resampling method, and a collaborative tracking mechanism was established based on the concept of resolution. The results showed that the proposed method had higher tracking precision and anti-occlusion performance than other existing methods. In this method, the robustness of tracking was effectively improved, and unlimited optimization cooperation between visual sensing was achieved.