A Winner-Take-All Strategy for Improved Object Tracking

Recently, numerous state-of-the-art learning schemes are proposed for object tracking. However, typically, most methods can only solve certain type of challenges but are less effective for the rest-no single tracker is perfect for all challenges. In this paper, a winner-take-all (WTA) strategy is exploited to select a winner tracker (considering both accuracy and efficiency) from a set of prevailing methods to tackle the current challenge, according to features extracted from the current environment and an efficiency factor. To achieve this, a structural regression model to characterize the trackers is trained on a public dataset. By incorporating the complementary abilities from multiple trackers, the diversity of the model is improved so that the WTA tracker can tackle various unpredictable difficulties. Since only one tracker is selected at any time, the average efficiency of the proposed model is also higher than that of complex trackers in the tracker set. The proposed WTA framework is tested on two benchmark datasets as well as several long sequences, and extensive experimental results illustrate that WTA can significantly improve both the performance and the efficiency.