Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking

TL;DR

This paper introduces a novel spatially supervised recurrent CNN approach for visual object tracking, leveraging temporal location history and deep features to improve accuracy and robustness.

Contribution

It proposes a new regression-based deep learning tracker that combines convolutional features with region information using LSTM, outperforming existing methods.

Findings

Achieves superior tracking accuracy on benchmark datasets.

Maintains low computational cost compared to state-of-the-art methods.

Outperforms competing trackers by a large margin on most sequences.

Abstract

In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking. Our recurrent convolutional network exploits the history of locations as well as the distinctive visual features learned by the deep neural networks. Inspired by recent bounding box regression methods for object detection, we study the regression capability of Long Short-Term Memory (LSTM) in the temporal domain, and propose to concatenate high-level visual features produced by convolutional networks with region information. In contrast to existing deep learning based trackers that use binary classification for region candidates, we use regression for direct prediction of the tracking locations both at the convolutional layer and at the recurrent unit. Our extensive experimental results and performance comparison with state-of-the-art tracking methods on challenging benchmark video tracking datasets shows that our tracker is more accurate and robust while maintaining low computational cost. For most test video sequences, our method achieves the best tracking performance, often outperforms the second best by a large margin.