Adaptive homochromous disturbance elimination and feature selection based mean-shift vehicle tracking method

Jie Ding; Bo Lei; Pu Hong; Chensheng Wang

doi:10.1117/12.904669

28 November 2011 Adaptive homochromous disturbance elimination and feature selection based mean-shift vehicle tracking method

Jie Ding, Bo Lei, Pu Hong, Chensheng Wang

Author Affiliations +

Proceedings Volume 8200, 2011 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology; 820005 (2011) https://doi.org/10.1117/12.904669
Event: International Conference on Optical Instruments and Technology (OIT2011), 2011, Beijing, Beijing, China

Abstract

This paper introduces a novel method to adaptively diminish the effects of disturbance in the airborne camera shooting traffic video. Based on the moving vector of the tracked vehicle, a search area in the next frame is predicted, which is the area of interest (AOI) to the mean-shift method. Background color estimation is performed according to the previous tracking, which is used to judge whether there is possible disturbance in the predicted search area in the next frame. Without disturbance, the difference image of vehicle and background could be used as input features to the mean-shift algorithm; with disturbance, the histogram of colors in the predict area is calculated to find the most and second disturbing color. Experiments proved this method could diminish or eliminate the effects of homochromous disturbance and lead to more precise and more robust tracking.

Citation Download Citation

Jie Ding, Bo Lei, Pu Hong, and Chensheng Wang "Adaptive homochromous disturbance elimination and feature selection based mean-shift vehicle tracking method", Proc. SPIE 8200, 2011 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 820005 (28 November 2011); https://doi.org/10.1117/12.904669

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KEYWORDS

Detection and tracking algorithms

Roads

Video

Feature selection

Image processing

Evolutionary algorithms

Cameras

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