Abstract
This paper presents the projective particle filter, a Bayesian filtering technique integrating the projective transform, which describes the distortion of vehicle trajectories on the camera plane. The characteristics inherent to traffic monitoring, and in particular the projective transform, are integrated in the particle filtering framework in order to improve the tracking robustness and accuracy. It is shown that the projective transform can be fully described by three parameters, namely, the angle of view, the height of the camera, and the ground distance to the first point of capture. This information is integrated in the importance density so as to explore the feature spacemore accurately. By providing a fine distribution of the samples in the feature space, the projective particle filter outperforms the standard particle filter on different tracking measures. First, the resampling frequency is reduced due to a better fit of the importance density for the estimation of the posterior density. Second, the mean squared error between the feature vector estimate and the true state is reduced compared to the estimate provided by the standard particle filter. Third, the tracking rate is improved for the projective particle filter, hence decreasing track loss.
Highlights
Introduction and MotivationsVehicle tracking has been an active field of research within the past decade due to the increase in computational power and the development of video surveillance infrastructure
Since the two vehicle tracking algorithms possess the same architecture, the difference in performance can be attributed to the distribution of particles through the importance density integrating the projective transform
The algorithm is tested on 15 traffic monitoring video sequences, labeled Video 001 to Video 015 in Algorithm 1
Summary
Introduction and MotivationsVehicle tracking has been an active field of research within the past decade due to the increase in computational power and the development of video surveillance infrastructure. The area of Intelligent Transportation Systems (ITSs) is in need for robust tracking algorithms to ensure that top-end decisions such as automatic traffic control and regulation, automatic video surveillance and abnormal event detection are made with a high level of confidence. Accurate trajectory extraction provides essential statistics for traffic control, such as speed monitoring, vehicle count, and average vehicle flow. As a low-level task at the bottom-end of ITS, vehicle tracking must provide accurate and robust information to higher-level modules making intelligent decisions. In this sense, intelligent transportation systems are a major breakthrough since they alleviate the need for devices that can be prohibitively costly or unpractical to implement. Robust vehicle tracking is necessary to ensure effective performance
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