Abstract
In this chapter, we present a home-monitoring oriented human activity recognition benchmark database, based on the combination of a color video camera and a depth sensor. Our contributions are two-fold: (1) We have created a human activity video database named RGBD-HuDaAct, which contains synchronized color-depth video streams, for the task of human daily activity recognition. This database aims at encouraging research in human activity recognition based on multi-modal video data (color plus depth). (2) We have designed two multi-modality fusion schemes which naturally combine color and depth information from two state-of-the-art feature representation methods for action recognition, namely, spatio-temporal interest points (STIPs) and motion history images (MHIs). These depth-extended feature representation methods are evaluated comprehensively, and superior recognition performance related to their uni-modal (color only) counterparts is demonstrated.KeywordsGaussian Mixture ModelActivity RecognitionInterest PointHuman Activity RecognitionVideo DatabaseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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