Recently, a tracking algorithm called Multiple Instance Learning Tracker (MILTrack) has become one of the most popular methods in treating tracking problems. This technique uses an MIL based appearance model to represent training data in the form of bags. It is commonly known that during tracking, slightly inaccuracies of locations may lead to incorrectly labeled training examples, which will cause model drift problem. MILTrack is designed to alleviate this problem. In this paper, we analyze the algorithm and point out that MILTrack has a serious problem that will reduce its performance. Then a solution to this problem is proposed. Experimental results showed that our enhanced MILTrack outperformed the original one.
['Xincan Li', 'Ming Tang']
Analysis of MILTrack