This paper presents a novel vehicle color classification technique for classifying vehicles into seven categories under different lighting conditions via color correction. First, to reduce lighting effects, a mapping function is built to minimize the color distortions between frames. In addition to color distortions, the effect of specular highlights can also make the window of a vehicle appear white and degrade the accuracy of vehicle classification. To reduce this effect, a window-removal task is performed to make vehicle pixels with the same color more concentrated on the analyzed vehicle. Thus, a vehicle can be more accurately classified into its corresponding category even when it is shone by strong sunlight. One major problem in vehicle color classification is that there are many shade colors; for example, white versus silver and black versus navy. Traditional methods lack the ability to classify vehicles with shade colors because a wrong classifier is designed by putting vehicles with the same label together even though their chromatic attributes are different. To treat this problem, a novel tree-based classifier is designed for classifying vehicles into chromatic/nonchromatic classes with their nonchromatic strengths and then into detailed color classes with their color features. The separation can significantly improve the accuracy of vehicle color classification even that vehicles are with various shade colors and captured under different lighting conditions. Index Terms-Vehicle color classification, color correction, SVM, vehicle window removal.
This paper proposes a novel Shift with Importance Sampling (SIS) scheme to improve the efficiency in pedestrian detection but maintain its high accuracy. For fast and efficient object detection, the cascade-Adaboost structure is the commonly-used approach in the literature. However, its detection performance is quite lower due to non-robust features and a fully-scanning on image especially when deformable part models are adopted. Firstly, various SURF points are first detected and then clustered via the K-Means scheme to produce potential candidates. Each pedestrian candidate is verified by a SVM-classifier based on HOG features. However, each SURP point will not exactly locate in the center of each detected pedestrian and lead to the failure of detection. To speed up the detection efficiency, we propose a novel Shift with Importance Sampling technique (SIS) to quickly shift into the correct location of each pedestrian with minimum tries and tests. The time complexity is reduced from 2 ( ) O n to (log ) O n . After that, the particle filter is adopted to track targets if they are missed. Experimental results show the superiority of our SIS method in pedestrian detection.
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