MPMF: multi-part multi-feature based object tracking

Abstract

The objective of tracking is to determine the states of an object in video frames while maintaining appearance and motion consistency. In this paper, we propose a novel multi-part multi-feature (MPMF) based object tracking which falls in the category of part-based trackers. We represent a target by a set of fixed parts (not semantic as body parts such as limbs, face) and each part is represented by a set of features. The multi-part representation of the object aids in partial occlusion handling and the multi-feature based object description increases robustness of the target representation. Instead of considering all the features of the parts, we measure tracker's confidence for a candidate by utilizing only the strong features of the candidate. This ensures that weak features do not interfere in the decision making. We also present an automatic method for selecting this subset of appropriate features for each part. To increase the tracker's speed and to reduce the number of erroneous candidates, we do not search in the whole frame. We keep the size of search area adaptive that depends on the tracker's confidence for the predicted location of the object. Additionally, it is easy to integrate more parts and features to the proposed tracker. The results on various challenging videos from VOT dataset are encouraging. MPMF outperforms state-of-the-art trackers on some of the standard challenging videos.