Learning Adaptive Selection Network for Real-Time Visual Tracking

Offline-trained trackers based on convolutional neural networks (CNNs) have shown great potential in achieving balanced accuracy and real-time speed. However, offline-trained trackers are prone to drift to background clutters. In this paper, we present an adaptive selection network tracker (ASNT) to address the tracking drift problem. Inspired by feature selection technique used in other vision problems, we introduce a learnable selection unit for Siamese network based trackers. The selection unit enables the tracker to select relevant feature map automatically for the target. Channel dropout is applied in the selection unit to improve generalization performance for convolutional layers. To further improve the discrimination between background clutters and the target, an adaptive method is used to initialize the tracker for each video sequence. Experiments on OTB-2013 and VOT2014 datasets demonstrate that our ASNT tracker has a comparable performance against state-of-the-art methods, yet can run at a speed of over 100 fps.