This paper presents a new technique to segment objects of interest from cluttered background with varying edge densities and illumination conditions from gray scale imagery. An optimal background model is generated and an index of disparity of the objects from this model is computed. This index estimates the disparity, both in terms of edge densities and edge orientation. We introduce Feature B...

This paper presented a new human gait classification based on the notion that gait types can be analyzed into a series of consecutive postures types. Silhouettes are extracted using the background subtraction method and then human posture silhouettes features are represented by moment. In the learning stage, we propose a method for the establishment of the standard gait base matrix by the metho...

In this paper, a pixel-based background modeling method, which uses nonparametric kernel density estimation, is proposed. To reduce the burden of image storage, we modify the original KDE method by using the first frame to initialize it and update it subsequently at every frame by controlling the learning rate according to the situations. We apply an adaptive threshold method based on image cha...

This paper presents a compressed-domain fall incident detection scheme for intelligent homecare applications. For object extraction, global motion parameters are estimated to distinguish local object motions from camera motions so as to obtain a rough object mask. We then perform change detection and/or background subtraction on the DC+2AC images extracted from the incoming coded bitstream to r...

The classical hypothesis, that bottom-up saliency is a center-surround process, is combined with a more recent hypothesis that all saliency decisions are optimal in a decision-theoretic sense. The combined hypothesis is denoted as discriminant center-surround saliency, and the corresponding optimal saliency architecture is derived. This architecture equates the saliency of each image location t...

This paper presents a novel approach for eliminating unexpected shadows from multiple pedestrians from a static and textured background using Gaussian shadow modeling. First, a set of moving regions are segmented from the static background using a background subtraction technique. The extracted moving region may contain multiple shadows from various pedestrians. In order to remove these unwante...

In this paper, a monitoring system that can distinguish the normal behavior from abnormal ones based on trajectory of palm for supermarket is designed. Our system brings some traditional algorithms and insights together to construct a framework for a new field called Supermarket Monitoring. In this project, only the moving hands are considered. To fulfill the automated monitoring task, the self...

Detection is an inherent part of every advanced automatic tracking system. In this work we focus on automatic detection of humans by enhanced background subtraction. Background subtraction (BS) refers to the process of segmenting moving regions from video sensor data and is usually performed at pixel level. In its standard form this technique involves building a model of the background and extr...

The basis for the high-level interpretation of observed patterns of human motion still relies on motion segmentation. Popular approaches based on background subtraction use colour information to model each pixel during a training period. Nevertheless, a deep analysis on colour segmentation problems demonstrates that colour segmentation is not enough to detect all foreground objects in the image...