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Recently, human action recognition in videos attracts increasing research interests in computer vision. As a result, visual features are becoming crucial for human action recognition in videos. In this paper, we propose Space-Time Binarized Normed Gradients (ST-BING) as a new feature for action recognition and localization. This feature comprises both the static information and the non-static information of an action performer. We use our ST-BING feature extracted from human action boundingdoi:10.24507/icicelb.07.05.993 fatcat:2tawvh63jncofbnifspznpvzta