Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends

Abstract

In recent years visual object tracking has become a very active research area. An increasing number of tracking algorithms are being proposed each year. It is because tracking has wide applications in various real world problems such as humancomputer interaction, autonomous vehicles, robotics, surveillance and security just to name a few. In the current study, we review latest trends and advances in the tracking area and evaluate the robustness of di erent trackers based on the feature extraction methods. e rst part of this work comprises a comprehensive survey of the recently proposed trackers. We broadly categorize trackers into Correlation Filter based Trackers (CFTs) and Non-CFTs. Each category is further classi ed into various types based on the architecture and the tracking mechanism. In the second part, we experimentally evaluated 24 recent trackers for robustness, and compared handcra ed and deep feature based trackers. We observe that trackers using deep features performed be er, though in some cases a fusion of both increased performance signi cantly. In order to overcome the drawbacks of the existing benchmarks, a new benchmark Object Tracking and Temple Color (OTTC) has also been proposed and used in the evaluation of di erent algorithms. We analyze the performance of trackers over eleven di erent challenges in OTTC, and three other benchmarks. Our study concludes that Discriminative Correlation Filter (DCF) based trackers perform be er than the others. Our study also reveals that inclusion of di erent types of regularizations over DCF o en results in boosted tracking performance. Finally, we sum up our study by pointing out some insights and indicating future trends in visual object tracking eld.