Abstract
Recently, convolutional neural network (CNN) models have achieved great success in many vision tasks. However, few attempts have been made to explore CNN for online model-free object tracking without time-consuming offline training. In this paper, we propose an online convolutional network (OC-N) for visual object tracking. To make the network less dependent on labeled data, K-means is employed to learn multistage filter banks for hierarchical feature learning. To preserve more spatial information for tracking, down-sampling and pooling operations are eliminated, which enables our system more sensitive to spatial variations. A regression model is adopted as the output layer of OCN to predict the position changes of the target. To deal with the stability-plasticity dilemma, two OCNs with different update rates are integrated to construct an ensemble framework. Experiments on challenging benchmark video sequences demonstrate that the proposed tracker outperforms several state-of-the-art methods.