Attentive Deep Regression Networks for Real-Time Visual Face Tracking in Video Surveillance
This addresses the problem of robust face tracking for video surveillance systems, though it is incremental as it builds on existing trackers.
The authors tackled visual face tracking in video surveillance by proposing an attentive deep learning tracker based on GOTURN, which outperformed state-of-the-art trackers by large margins and achieved speeds far beyond real-time requirements.
Visual face tracking is one of the most important tasks in video surveillance systems. However, due to the variations in pose, scale, expression, and illumination it is considered to be a difficult task. Recent studies show that deep learning methods have a significant potential in object tracking tasks and adaptive feature selection methods can boost their performance. Motivated by these, we propose an end-to-end attentive deep learning based tracker, that is build on top of the state-of-the-art GOTURN tracker, for the task of real-time visual face tracking in video surveillance. Our method outperforms the state-of-the-art GOTURN and IVT trackers by very large margins and it achieves speeds that are very far beyond the requirements of real-time tracking. Additionally, to overcome the scarce data problem in visual face tracking, we also provide bounding box annotations for the G1 and G2 sets of ChokePoint dataset and make it suitable for further studies in face tracking under surveillance conditions.