Comparison of General Object Trackers for Hand Tracking in High-Speed Videos

The problem of tracking a hand in video has gained a lot of attention due to its numerous applications in human computer interfaces. So far, the work has been limited to the use of standard speed videos, but the recent developments in imaging technology and computing hardware have made it attractive to exploit high-speed imaging for tracking the hand more accurately both in space and time. To produce videos of good quality, the high-speed imaging requires more light when compared to imaging with conventional frame rates. Therefore, grey-scale high-speed imaging is in common use and this makes the use of hand tracking methods relying specifically on color information unsuitable. In this work, we provide the first solid comparison of state-of-the-art general object trackers on hand tracking with a primary focus on grey-scale high-speed videos. Novel annotated high-speed video data were collected and made publicly available for evaluation purposes. The algorithms were tested with both finger and hand targets, and with grey-scale and color videos. In addition to tracking accuracies, the stability, sensitivity, and the processing speeds of the algorithms were evaluated. The experiments show that the results vary significantly in all aspects, but certain methods such as Compressive Tracking and Hough Track methods performed better overall.