時空間情報と距離情報を用いたJoint Boostingによる動作識別

概要

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This paper presents a method for action classification by using Joint Boosting with depth information obtained by TOF camera. Our goal is to classify action of a customer who takes the goods from each of the upper, middle and lower shelf in the supermarkets and convenience stores. Our method detects of human region by using Pixel State Analysis (PSA) from the depth image stream obtained by TOF camera, and extracts the PSA features captured from human-motion and the depth features (peak value of depth) captured from the information of human-height. We employ Joint Boosting, which is a multi-class classification of boosting method, to perform the action classification. Since the proposed method employs spatiotemporal and depth feature, it is possible to perform the detection of action for taking the goods and the classification of the height of the shelf simultaneously. Experimental results show that our method using PSA feature and peak value of depth achieved a classification rate of 93.2%. It also had a 3.1% higher performance than that of the CHLAC feature, and 2.8% higher performance than that of the ST-patch feature.