关键词: 多伯努利滤波, 卷积特征, 自适应学习, 视频多目标跟踪

Abstract: Multi-Bernoulli (MB) filter based on stochastic finite set theory has been demonstrated as a promising algorithm for tracking multiple targets with unknown and time-varying number of objects. However, for visual multi-object tracking (VMOT) in a complex scenario, it is difficult to solve the interference between close targets and background clutter. Especially, when the targets are close to each other and occluded, it will lead to the decrease of tracking accuracy and even target missed tracking. To solve these problems, under the framework of multi-Bernoulli (MB) filter, deeply analyzing the feature information of the target and introducing the anti-interference convolution feature, this paper proposes an effective VMOT algorithm by integrating convolution features into the framework of MB filter. Moreover, in the process of object state extraction, the adaptive scheme of template update is proposed by using an adaptive learning rate, which makes the template adapt to the scale variation and handle the problem of closely-spaced object tracking effectively. Finally, the particle labelling technique is employed to realize visual multi-object tracking. Experimental results show that, the proposed algorithm can effectively distinguish the closely-spaced object in a complex scenario, and has a high tracking accuracy.

Key words: multi-Bernoulli (MB) filter, convolution feature, adaptive learning, visual multi-object tracking