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Visual face tracking is one of the most important tasks in video surveillancesystems. However, due to the variations in pose, scale, expression, andillumination it is considered to be a difficult task. Recent studies show thatdeep learning methods have a significant potential in object tracking tasks andadaptive feature selection methods can boost their performance. Motivated bythese, we propose an end-to-end attentive deep learning based tracker, that isbuild on top of the state-of-the-art GOTURN tracker, for the task of real-timevisual face tracking in video surveillance. Our method outperforms thestate-of-the-art GOTURN and IVT trackers by very large margins and it achievesspeeds that are very far beyond the requirements of real-time tracking.Additionally, to overcome the scarce data problem in visual face tracking, wealso provide bounding box annotations for the G1 and G2 sets of ChokePointdataset and make it suitable for further studies in face tracking undersurveillance conditions.

Attentive Deep Regression Networks for Real-Time Visual Face Tracking in Video Surveillance