CNNTracker: Online discriminative object tracking via deep convolutional neural network

Abstract

Object appearance model is a crucial module for object tracking and numerous schemes have been developed for object representation with impressive performance. Traditionally, the features used in such object appearance models are predefined in a handcrafted offline way but not tuned for the tracked object. In this paper, we propose a deep learning architecture to learn the most discriminative features dynamically via a convolutional neural network (CNN). In particular, we propose to enhance the discriminative ability of the appearance model in three-fold. First, we design a simple yet effective method to transfer the features learned from CNNs on the source tasks with large scale training data to the new tracking tasks with limited training data. Second, to alleviate the tracker drifting problem caused by model update, we exploit both the ground truth appearance information of the object labeled in the initial frames and the image observations obtained online. Finally, a heuristic schema is used to judge whether updating the object appearance models or not. Extensive experiments on challenging video sequences from the CVPR2013 tracking benchmark validate the robustness and effectiveness of the proposed tracking method.