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Reliable people counting and human detection is an important problem in visual surveillance. In recent years, the field has seen many advances, but the solutions have restrictions: people must be moving, the background must be simple, and the image resolution must be high. This paper aims to develop an effective method for estimating the number of people(More)
Reliable estimation of people in public areas is an important problem in visual surveillance. Although there is a lot of research on people counting in recent years, most of them consider a small crowd of people without many serious occlusions. Some of them have a lot of particular requirements, like people are moving, the background is smooth or the image(More)
Although there have been a lot of studies on human detection in recent years, most of them have some particular requirements. However, some videos may have complicated scenes with low resolution and include both moving and standing people. The paper aims for individual detection under the challenging situation. An EM (Expectation-Maximum) based method has(More)
In this paper, our focus is to segment the foreground area for human detection. It is assumed that the foreground region has been detected. Accurate foreground contours are not required. The developed approach adopts a modified ISM (Implicit Shape Model) to collect some typical local patches of human being and their location information. Individuals are(More)
In this paper, our aim is to segment a foreground region into individual persons in crowded scenes. We will focus on the combination of multiple clues for crowd segmentation. To ensure a wide range of applications, few assumptions are needed on the scenarios. In the developed method, crowd segmentation is formulated as a process to group the feature points(More)
People counting is a usual problem in visual surveillance. An accurate and real-time estimation of people in a crowded place can provide valuable information. Here video is inputted and gives the average number of people as output. The video input is separated to number of frames and some processing steps are performed on background subtraction results to(More)
To suppress the heavy noise and keep the distinct edges of the images in the low light condition, we propose a denoising model based on the combination of total variation (TV) and nonlocal similarity in the wavelet domain. The TV regularization in the wavelet domain effectively suppresses the heavy noise with the biorthogonal wavelet function; the nonlocal(More)
Crowd segmentation is an important topic in a visual surveillance system. In this paper, crowd segmentation is formulated as a problem to cluster the feature points inside the foreground region with a set of rectangles. Coherent motion of feature points in an individual are fused with appearance cues around the feature points for crowd segmentation, which(More)
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