Detecting subtle human-object interactions using kinect

Abstract

We present a method to identify human-object interactions involved in complex, fine-grained activities. Our approach benefits from recent improvements in range sensor technology and body trackers to detect and classify important events in a depth video. Combining global motion information with local video analysis, our method is able to recognize the time instants of a video at which a person picks up or puts down an object. We introduce three novel datasets for evaluation and perform extensive experiments with promising results.