Visual Tracking by Structurally Optimizing Pre-Trained CNN

Abstract

In this paper, we propose a novel channel pruning method for convolutional neural network (CNN)-based trackers. Pre-trained CNNs are widely used in visual tracking to obtain high-level representations of targets. However, most pre-trained CNNs are trained for other tasks (e.g., VGGNet is trained for image classification), and they require a considerable amount of time to generate features. First, we introduce a dimensionality reduction method considering the information amount and tracking errors to obtain good low-dimensionality features from the last convolutional layer for tracking. Then, a backward channel selection method is proposed to select representative channels layer by layer. In this process, we aim to minimize the target changes and maximize the loss of the background or other objects. Finally, we reconstruct the neural network weights to reduce the information loss of the target with one-shot learning. Experimental results on challenging benchmarks show that the proposed channel pruning method can enhance the tracking performance and reduce the computational requirements.