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)
Object tracking in the video sequence is a challenging task because of its various applications in video compression, video surveillance, robot technology etc. Several object tracking methods exist in spatial and wavelet domain, to work with rigid and non-rigid object boundaries. Spatial domain tracking techniques are not accurate as well as they are slow(More)
This paper proposes complex wavelet-based moving object segmentation using approximate median filter based method. The proposed method is well capable of dealing with the drawbacks such as ghosts, shadows and noise present in other spatial domain methods available in literature. The performance of the proposed method is evaluated and compared with other(More)
This paper presents a framework for classification and recognition of human activities in complex motion. We propose a template matching based method to classify the objects and a rule-based approach to recognize human activities. First, moving objects are detected and their silhouettes are generated in each frame. Second, template matching based approach(More)
In this paper, we describe a novel template matching based approach for recognition of different human activities in a video sequence. We model the background in the scene using a simple statistical model and extract the foreground objects present in a scene. The matching templates are constructed using the motion history images (MHI) and spatial(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)
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)
Object classification is an important problem in computer vision, in which multiclass object classification is more difficult one in comparison to single class object classification. In this paper, we proposed a new method for multiclass object classification based on discrete wavelet transform. We have used discrete wavelet transform coefficients as a(More)