Learning Good Features to Track
Raed Almomani, Ming Dong, Zhou Liu · 2014
Object tracking is an important task within the field of computer vision. Tracking accuracy depends mainly on finding good discriminative features to estimate the target location. In this paper, we introduce online feature learning in tracking and propose to learn good features to track generic objects using online convolutional neural networks (OCNN). OCNN has two feature mapping layers that are trained offline based on unlabeled data. In tracking, the collected positive and negative samples from the previously tracked frames are used to learn good features for a specific target. OCNN is also augmented with a classifier to provide a decision. We build a tracking system by combining OCNN and a color-based multi-appearance model. Our experimental results on publicly available video datasets show that the tracking system has superior performance when compared with several state of-the-art trackers.