“…This module takes into account pixel differences between consecutive frames and the frame-byframe decisions accumulation. In [4], a scheme based on chromaticity distortion is presented. This system is able to detect shadows, highlights and foreground regions.…”
Section: Previous Related Workmentioning
confidence: 99%
“…For example, in [3] a system that accumulates temporal foreground results and a neural network classifier are used to discriminate between people, lighting effects and unattended objects. In [4] a multiple-state model of an unattended package provides the ability to detect realistic unattended package events. In [7] a long-term logic is used to differentiate between unattended objects and stationary people, and is robust to temporary occlusion of potential unattended objects.…”
“…This module takes into account pixel differences between consecutive frames and the frame-byframe decisions accumulation. In [4], a scheme based on chromaticity distortion is presented. This system is able to detect shadows, highlights and foreground regions.…”
Section: Previous Related Workmentioning
confidence: 99%
“…For example, in [3] a system that accumulates temporal foreground results and a neural network classifier are used to discriminate between people, lighting effects and unattended objects. In [4] a multiple-state model of an unattended package provides the ability to detect realistic unattended package events. In [7] a long-term logic is used to differentiate between unattended objects and stationary people, and is robust to temporary occlusion of potential unattended objects.…”
“…Stauffer and Grimson [27] present an event detection module that classifies objects, including abandoned objects, using a neural network, but is limited to detecting only one abandoned object at a time. In [28] a multicamera video surveillance system is proposed to detect the owner of each abandoned object is determined and tracked using distance and time constraints and multiple cameras with overlapping fields of view are exploited to cope with occlusion of various types.…”
Abstract:Object detection is an important step in any video analysis. Difficulties of the object detection are finding hidden objects and finding unrecognized objects. Although many algorithms have been developed to avoid them as outliers, occlusion boundaries could potentially provide useful information about the scene's structure and composition. A novel framework for blob based occluded object detection is proposed. A technique that can be used to detect occlusion is presented. It detects and tracks the occluded objects in video sequences captured by a fixed camera in crowded environment with occlusions. Initially the background subtraction is modeled by a Mixture of Gaussians technique (MOG). Pedestrians are detected using the pedestrian detector by computing the Histogram of Oriented Gradients descriptors (HOG), using a linear Support Vector Machine (SVM) as the classifier. In this work, a recognition and tracking system is built to detect the abandoned objects in the public transportation area such as train stations, airports etc. Several experiments were conducted to demonstrate the effectiveness of the proposed approach. The results show the robustness and effectiveness of the proposed method.
“…Generally, there are three main approaches to classification: shape-based classification (e.g., [1,[8][9][10]12]), motion-based classification (e.g., [3]) or combined shape-motion classification (e.g., [2,6,13]). …”
Section: Background and Related Workmentioning
confidence: 99%
“…To date, in the visual surveillance research area, the literature reports attempts to analyze four main categories of objects, namely, person, vehicle, group of people, and package (e.g., [1,2,6,[8][9][10][12][13]). …”
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