Learning a Dynamic Feature Fusion Tracker for Object Tracking

Abstract

Object tracking is a key component of self-driving systems and has important meanings to alleviate traffic accidents. Therefore, it is meaningful to design a high performance and real-time tracker for improving the stability and safety of self-driving systems. In this paper, an effective and efficient feature fusion tracker, which dynamically fuses gradient and color features to model the appearance of the target object, is designed with the correlation filters framework for fast tracking. To be specific, two complementary correlation filters for gradient (e.g. HOG) and color (e.g. ColorNames) features are maintained during tracking, and the proposed feature fusion method adaptively adjusts the weights of them to deal with large appearance changes of the target object in challenging tracking scenes. The weights are decided by the consistency of the final tracking result and the predicted results obtained by two correlation filters. Moreover, a failure detection scheme is designed to alleviate the model drift issue caused by undesirable model updates to improve the tracking accuracy. If a tracking result is identified as a failed case, re-detection operations are performed to accurately localize the target object. The experimental results prove that the proposed tracker can achieve competitive tracking performance and a satisfactory tracking speed of 25.3 FPS in comparison with several state-of-the-art trackers on challenging tracking benchmarks.