We study the problem of humanactivity recognition from RGB-Depth(RGBD)sensors when the skeletons are not available.The skeleton tracking in Kinect SDK workswell when the human subject is facing thecamera and there are no occlusions.In surveillance or nursing home monitoring scenarios,however,the camera is usually mounted higher than human subjects,and there may beocclusions.The interest-point based approachis widely used in RGB based activity recognition,it can be used in both RGB and depthchannels.Whether we should extract interestpoints independently of each channel or extract interest points from only one of thechannels is discussed in this paper.The goal ofthis paper is to compare the performances ofdifferent methods of extracting interest points.In addition,we have developed a depth mapbased descriptor and built an RGBD dataset,called RGBD-SAR,for senior activity recognition.We show that the best performance isachieved when we extract interest points solely from RGB channels,and combine the RGBbased descriptors with the depth map-baseddescriptors.We also present a baseline performance of the RGBD-SAR dataset.
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