Hand Detection and Tracking in Videos for Fine-Grained Action Recognition
نویسندگان
چکیده
In this paper, we develop an effective method of detecting and tracking hands in uncontrolled videos based on multiple cues including hand shape, skin color, upper body position and flow information. We apply our hand detection results to perform fine-grained human action recognition. We demonstrate that motion features extracted from hand areas can help classify actions even when they look familiar and they are associated with visually similar objects. We validate our method of detecting and tracking hands on VideoPose2.0 dataset and apply our method of classifying actions to the playing-instrument group of UCF101 dataset. Experimental results show the effectiveness of our approach.
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