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The following topics are dealt with: video surveillance; signal based surveillance; motion detection; traffic surveillance; face recognition; object tracking; data fusion; intelligent sensors and applications.
Traffic data extraction is an increasing demand for applications such as traffic lights control, population evacuation, or to reduce traffic issues including congestion, pollution, delays, and accidents. We present in this paper a new framework to reliably detect, classify and track multiple vehicles at night-time. The system shows excellent performance after an evaluation procedure involving many...
This article introduces a new particle filtering approach for object tracking in video sequences. The projective particle filter uses a linear fractional transformation, which projects the trajectory of an object from the real world onto the camera plane, thus providing a better estimate of the object position. In the proposed particle filter, samples are drawn from an importance density integrating...
Information on the vehicular traffic density in an intelligent transport system (ITS) is presently obtained mainly through loop detectors (LD), traffic radars and surveillance cameras. However, the difficulties and cost of installing loop detectors and traffic radars tend to be significant. Currently, a more advanced method of circumventing this is to develop a sort of virtual loop detector (VLD)...
Background modeling has been widely researched to detect moving objects from image sequences. It is necessary to adapt the background model various changes of illumination condition. A hybrid type of background model which consists of more than one background model has been used for object detection since it is very robust for illumination changes. In this paper, we also propose a new hybrid type...
In several video surveillance applications, such as the detection of abandoned/stolen objects or parked vehicles,the detection of stationary foreground objects is a critical task. In the literature, many algorithms have been proposed that deal with the detection of stationary foreground objects, the majority of them based on background subtraction techniques. In this paper we discuss various stationary...
A common method for real time moving object detection in image sequences is background removal, also referred to as background subtraction. The numerous approaches differ in the type of background model used and the procedure used to update the model. This paper discusses modeling each 4 times 4 pixel patch of an image through a set of coefficient vectors which are obtained by means of a discrete...
This paper presents a method based on feature selection to obtain sets of human activity recognizers of different complexity. Classifiers for human activity recognition are built exploring a space of candidate feature subsets, trying to maximize the accuracy of a classifier trained with them. At the same time, the size of the selected feature subset is minimized. The accuracy of a classifier tends...
This paper addresses the problem of silhouette-based human action modeling and recognition, specially when the number of action samples is scarce. The first step of the proposed system is the 2D modeling of human actions based on motion templates, by means of motion history images (MHI). These templates are projected into a new subspace using the Kohonen self organizing feature map (SOM), which groups...
Human identification by recognizing the spontaneous gait recorded in real-world setting is a tough and not yet fully resolved problem in biometrics research. Several issues have contributed to the difficulties of this task. They include various poses, different clothes, moderate to large changes of normal walking manner due to carrying diverse goods when walking, and the uncertainty of the environments...
Human action recognition is a significant task in automatic understanding systems for video surveillance. Probabilistic Latent Semantic Analysis (PLSA) model has been used to learn and recognize human actions in videos. Specifically, PLSA employs the expectation maximization (EM) algorithm for parameter estimation during the training. The EM algorithm is an iterative estimation scheme that is guaranteed...
In this paper, we consider the challenging problem of unusual event detection in video surveillance systems. The proposed approach makes a step toward generic and automatic detection of unusual events in terms of velocity and acceleration. At first, the moving objects in the scene are detected and tracked. A better representation of moving objects trajectories is then achieved by means of appropriate...
Vision-based people counting systems have wide potential applications including video surveillance and public resources management. Most works in the literature rely on moving object detection and tracking, assuming that all moving objects are people. In this paper, we present our people counting approach based on face detection, tracking and trajectory classification. While we have used a standard...
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