Alok Kumar Singh Kushwaha

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Human activity recognition is a challenging area of research because of its various potential applications in visual surveillance. A spatio-temporal template matching based approach for activity recognition is proposed in this paper. We model the background in a scene using a simple statistical model and extract the foreground objects in a scene.(More)
This paper addresses the problem of silhouette-based human activity recognition. Most of the previous work on silhouette based human activity recognition focus on recognition from a single view and ignores the issue of view invariance. In this paper, a system framework has been presented to recognize a view invariant human activity recognition approach that(More)
In recent past, many moving object segmentation methods under varying lighting changes have been proposed in literature and each of them has their own benefits and limitations. The various methods available in literature for moving object segmentation may be broadly classified into four categories i.e., moving object segmentation methods based on (i) motion(More)
In this paper we present an algorithm for real-time object classification and human activity recognition which can help to made intelligent video surveillance systems for human behavior analysis. The proposed method makes use of object silhouettes to classify objects and activity of humans present in a scene monitored by a dynamic camera. An statical(More)
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