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Every year, traffic congestion and traffic accidents have been rapidly increasing in proportion to increasing number of vehicles. Although the roadway design and signal system have been improved to relieve traffic congestion, traffic casualties and property damage do not decrease. The traffic accident is a serious issue of society because vehicle is a primary means of transportation. This paper develops...
Recently, the problem of automatic traffic accident recognition has appealed to the machine vision community due to its implications on the development of autonomous Intelligent Transportation Systems (ITS). In this paper, a new framework for real-time automated traffic accidents recognition using Histogram of Flow Gradient (HFG) is proposed. This framework performs two major steps. First, HFG-based...
Automated tracking of vehicles and people is essential for the effective utilization of imagery in wide area surveillance applications. In order to determine the best tracking algorithm and parameters for a given application, a comprehensive evaluation procedure is required. However, despite half a century of research in multi-target tracking, there is no consensus on how to score the overall performance...
Correcting video sensing error problems for traffic surveillance was hard investigated over the last years. Several parameters such as light intensity, image quality and resolution, and in general all the key factors related to image capture were considered. We have investigated here a modified MCMC approach based on the influence of typical patterns of driving behavior. These patterns are tested...
Recently video surveillance techniques have been widely applied to intelligent transportation systems. Tracking of moving objects such as vehicles has become a major topic in video surveillance applications. This paper presents a multi-feature fusion model based on a particle filter for moving object tracking. The particle filter combines color and edge orientation information by a stochastic fusion...
Currently available methods for object recognition and classification primarily rely on static information in single-frame images. However, for the combat aerial video (usually low resolution video), all these static indexes used for object classification and recognition are almost impossible to obtain. To address this challenge, we propose an innovative 3D and dynamic semantic scene analysis based...
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