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Urban traffic surveillance, which is designed to improve traffic management, is an important part of intelligent traffic system (ITS). In particular, airborne moving vehicle detection has become a new but hot research area since its wide view and low cost. However, airborne urban traffic surveillance is impacted by many difficulties such as camera vibration, vehicle congestion, background variance,...
We present a comparative evaluation of the state-of-art algorithms for detecting pedestrians in low frame rate and low resolution footage acquired by mobile sensors. Four approaches are compared: a) The Histogram of Oriented Gradient (HoG) approach; b) A new histogram feature that is formed by the weighted sum of both the gradient magnitude and the filter responses from a set of elongated Gaussian...
Video surveillance systems produce huge amounts of data for storage and display. Long-term human monitoring of the acquired video is impractical and ineffective. Automatic abnormal motion detection system which can effectively attract operator attention and trigger recording is therefore the key to successful video surveillance in dynamic scenes, such as airport terminals. This paper presents a novel...
This paper presents a method of integration and implementation of transmitting video and audio data from multiple Internet protocol (IP) surveillance cameras in a wireless sensor network to a centralized management unit (central node) using real time streaming protocol (RTSP). The wireless network is based on ldquostarrdquo topology by using the IEEE 802.11 series of standards in wireless local area...
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 resolution is high. This paper aims to estimate...
Making intelligent decisions on the basis of the video captured by a large network of surveillance cameras requires the ability to identify overlap between their fields of view. Without this information it is impossible to perform even simple analysis, such as distinguishing between repeated behaviours and multiple views of the same behaviour. Large-scale intelligent video surveillance thus requires...
Detecting abnormal behaviors is a critical task today. We need to monitor large areas, manage camera sensor data, and use this data for detecting behaviors, detecting the abnormal behaviors and classifying the normal behaviors. In order to monitor large areas, we need multiple cameras across a large-scale network. We use an architecture for a network of clustered cameras to minimize and efficiently...
In this work we consider two problems for video surveillance applications: (a) abnormal behavior detection and (b) behavior matching across cameras. We propose busy-idle rates, meaningful and easy to compute features of foreground objects, to characterize the behavior profile of a given pixel. We use these features to model the typical behavior that is observed in training sequences. Using a small...
One of the decisive steps in automated surveillance and monitoring is object detection. A standard approach to constructing object detectors consists of annotating large data sets and using them to train a detector. Nevertheless, due to unavoidable constraints of a typical training data set, supervised approaches are inappropriate for building generic systems applicable to a wide diversity of camera...
One of the core components of any visual surveillance system is object classification, where detected objects are classified into different categories of interest. Although in airports or train stations, abandoned objects are mainly luggage or trolleys, none of the existing works in the literature have attempted to classify or recognize trolleys. In this paper, we analyzed and classified images of...
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