View-Invariant Human Detection from RGB-D Data of Kinect Using Continuous Hidden Markov Model

Abstract

AbstractIn this paper authors have presented a method to detect human from a Kinect captured Gray-Depth (G-D) using Continuous Hidden Markov models (C-HMMs). In our proposed approach, we initially generate multiple gray scale images from a single gray scale image/ video frame based on their depth connectivity. Thus, we initially segment the G image using depth information and then relevant components were extracted. These components were further filtered out and features were extracted from the candidate components only. Here a robust feature named Local gradients histogram(LGH) is used to detect human from G-D video. We have evaluated our system against the data set published by LIRIS in ICPR 2012 and on our own data set captured in our lab. We have observed that our proposed method can detect human from this data-set with a 94.25% accuracy.KeywordsGaussian Mixture ModelVideo FrameRecognition AccuracyDepth InformationHuman DetectionThese 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.