OSMO: Online Specific Models for Occlusion in Multiple Object Tracking under Surveillance Scene.

With demands of the intelligent monitoring, multiple object tracking (MOT) in surveillance scene has become an essential but challenging task. Occlusion is the primary difficulty in surveillance MOT, which can be categorized into the inter-object occlusion and the obstacle occlusion. Many current studies on general MOT focus on the former occlusion, but few studies have been conducted on the latter one. In fact, there are useful prior knowledge in surveillance videos, because the scene structure is fixed. Hence, we propose two models for dealing with these two kinds of occlusions. The attention-based appearance model is proposed to solve the inter-object occlusion, and the scene structure model is proposed to solve the obstacle occlusion. We also design an obstacle map segmentation method for segmenting obstacles from the surveillance scene. Furthermore, to evaluate our method, we propose four new surveillance datasets that contain videos with obstacles. Experimental results show the effectiveness of our two models.