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...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.展开更多
For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest p...For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest points is proposed to obtain the invariant local features, which is coined polynomial local orientation tensor(PLOT). The new detector is based on image local orientation tensor that is constructed from the polynomial expansion of image signal. Firstly, the properties of local orientation tensor of PLOT are analyzed, and a suitable tuning parameter of local orientation tensor is chosen so as to extract invariant features. The initial interest points are detected by local maxima search for the smaller eigenvalues of the orientation tensor. Then, an iterative procedure is used to allow the initial interest points to converge to affine invariant interest points and regions. The performances of this detector are evaluated on the repeatability criteria and recall versus 1-precision graphs, and then are compared with other existing approaches. Experimental results for PLOT show strong performance under affine transformation in the real-world conditions.展开更多
基金supported by the National Natural Science Foundation of China under Grants No.61075045,No.61273256the Program for New Century Excellent Talents in University under Grant No.NECT-10-0292+1 种基金the National Key Basic Research Program of China(973Program)under Grant No.2011-CB707000the Fundamental Research Funds for the Central Universities
文摘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.
基金Projects(61203332,61203208) supported by the National Natural Science Foundation of China
文摘For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest points is proposed to obtain the invariant local features, which is coined polynomial local orientation tensor(PLOT). The new detector is based on image local orientation tensor that is constructed from the polynomial expansion of image signal. Firstly, the properties of local orientation tensor of PLOT are analyzed, and a suitable tuning parameter of local orientation tensor is chosen so as to extract invariant features. The initial interest points are detected by local maxima search for the smaller eigenvalues of the orientation tensor. Then, an iterative procedure is used to allow the initial interest points to converge to affine invariant interest points and regions. The performances of this detector are evaluated on the repeatability criteria and recall versus 1-precision graphs, and then are compared with other existing approaches. Experimental results for PLOT show strong performance under affine transformation in the real-world conditions.
