Tracking Vulnerable Road Users with Severe Occlusion by Adaptive Part Filter Modeling

Abstract

The visual based tracking has been an active area among the research community in recent years. Its application can be broadly found in intelligent transportation systems like advanced driver assistance systems (ADAS) or autonomous vehicles. One objective is to monitor the trajectory and behavior of the vulnerable road users (VRUs), e.g., pedestrians and cyclists, to prevent the probable collisions. However, the tracking of VRUs is still challenging, especially in the cases, where severe occlusion occurs and the tracker fails due to the abrupt change of object appearance. To tackle this problem we propose a new tracking approach leveraging the part based trackers, which are built based on correlation filters. In this method both the number and the size of part filters are adapted to the current appearance of the object, to eliminate the influence by occluded parts. Experimental results show that our tracker performs robust with respect to occlusions, especially in cases, where long term and severe occlusions appear. Due to a sophisticated design, a real time processing performance can also be achieved by our approach.