GPU Based Video Object Tracking on PTZ Cameras

Cevahir Cigla, Kemal Emrecan Sahin, Fikret Alim; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 654-662


In this study, an embedded Pan-Tilt-Zoom (PTZ) tracker system design is proposed that is based on NVIDIA Tegra TK1-TX1 mobile GPU platform. For this purpose, state-of-the-art correlation filter (CF) based video object tracking (VOT) algorithms are exploited regarding their high performance. Each algorithmic step is carefully implemented on GPU that further increases the efficiency and decreases execution times. The PTZ control is designed to track human targets by centralizing within the image coordinates where the targets have limited speed but obvious appearance changes. Incorporating on-board decode and encode capability of Tegra platform as well as angular position control, the presented approach enables 50-100 fps target tracking for HD (1920x1080) videos on TK1 and TX1 correspondingly. This is to our best knowledge the first efficient implementation of CF trackers on a mobile GPU platform with use of multiple features, scale and background adaptation. This study extends the scope of accuracy focused VOT research to platform optimized efficient implementations for real-time high resolution video tracking.

Related Material

author = {Cigla, Cevahir and Emrecan Sahin, Kemal and Alim, Fikret},
title = {GPU Based Video Object Tracking on PTZ Cameras},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}