Automatic Traffic Surveillance System for Vehicle Tracking and Classification

Abstract

This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods which can classify vehicles to only cars and non-cars, the proposed method has a good capability to categorize vehicles into more specific classes by introducing a new "linearity" Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make the best decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only vehicle size and single frame for classification.

Vehicle Detection and Tracking Techniques : A Concise Review

“…The proposed method undertakes well the occlusion resulting from shades. Finally, this method represented an automatic vehicle tracking and classification traffic observation system [51].…”

Real-time hardware–software embedded vision system for ITS smart camera implemented in Zynq SoC

“…In the first case, objects are usually extracted using the differential image between the current frame and the so-called background model. This approach has been used, among others, in works [24,11,17,53,3]. Unfortunately, this solution has a number of drawbacks that hinder its practical application in traffic monitoring.…”

“…In the paper [24] two features: size and ''linearity'' were used. The first one was normalized with using vehicle position information.…”

Moving Cast Shadow Detection

“…The model is parameterized with several features including the orientation, mean intensity, and center position of a shadow region (as Fig.10 illustrates), with the orientation and centroid position being estimated from the properties of object moments. Hsieh et al (Hsieh et al, 2004;Hsieh et al, 2006) proposed a histogram-based method to detect different lane dividing lines from traffic video sequence. According to these lines, a line-based shadow modeling process is applied to eliminate the shadows of vehicles.…”