OsaMOT: Occlusion and scale‐aware multi‐object tracking algorithm for low viewpoint

Abstract

Multi-object tracking (MOT), which uses the context information of image sequences to locate, maintain identities and generate trajectories of multiple targets in each frame, is key technology in the field of computer vision. To address the problems of occlusion and scale variation in low-viewpoint MOT, OsaMOT is proposed here. First, according to the global occlusion state of each frame, OsaMOT proposes the adaptive anti-occlusion feature to enhance the awareness and adaptability for occlusion. At the same time, OsaMOT uses the cascade screening mechanism to reduce the “virtual new target” phenomenon due to the dramatic change in target features caused by scale variation and occlusion. Finally, considering that the occluded templates will affect the tracking performance, OsaMOT proposes an adaptive anti-noise template update mechanism according to the partial occlusion state of the target, which improves the purity of the template library and further enhances the applicability to occlusion. The experimental results show that OsaMOT can weaken the influence of scale variation, partial occlusion, short-term full occlusion and long-term full occlusion in the low-viewpoint tracking scenes. Most evaluation indexes of OsaMOT under low-viewpoint tracking scenario are superior to those of some typical algorithms proposed in recent years, and the tracking robustness is improved.