Swati Nigam

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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)
Human detection is a central problem in development of any surveillance application. In this study, we present a simple and efficient, multi-resolution gray scale invariant approach for multiple human detection. The multiresolution is important for objects of different size and gray scale invariance is important due to uneven illumination and within-class(More)
In this study, we advocate the importance of robust local features that allow object form to be distinguished from other objects for detection purpose. We start from the grid of Histogram of oriented gradients (HOG) and integrate Scale Invariant Feature Transform (SIFT) within them. In HOG features an object's appearance is detected by the distribution of(More)
Object recognition in real scenes is a central problem in computer vision. In this paper we propose a new approach for shape based recognition of objects in real scenes. This approach uses moment invariants for identification of shape features. Moment Invariants are functions of central moments. They are invariant against linear transformations such as(More)
In this paper, we have proposed a new object tracking method in video sequences which is based on curvelet transform. The wavelet transform has widely been used for object tracking purpose, but it cannot well describe curve discontinuities. We have used curvelet transform for tracking. Tracking is done using energy of curvelet coefficients in sequence of(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)
Moving object segmentation is an important step toward development of any computer vision systems. In the present work, we have proposed a new method for segmentation of moving objects, which is based on single change detection method applied on Contourlet coefficients of two consecutive frames. We have chosen contourlet transform as it has high(More)
In this study, we present a method for human activity recognition in video sequences. Human activities are often described by a holistic feature vector comprising of a set of local motion descriptors. Here, we use a novel local shape feature descriptor for human activity recognition which is an integration of moment invariants and uniform local binary(More)
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