Combining shape and texture features for infrared pedestrian detection

Hao Cui; Biao Li; Zhenkang Shen

doi:10.1117/12.902013

8 December 2011 Combining shape and texture features for infrared pedestrian detection

Hao Cui, Biao Li, Zhenkang Shen

Proceedings Volume 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis; 80021D (2011) https://doi.org/10.1117/12.902013
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

This paper presents a robust pedestrian detection algorithm that works on infrared imageries. Our algorithm is applicable to images captured from surveillance infrastructure as well as moving platforms. Firstly, we introduce a local binary pattern (LBP) texture feature for infrared pedestrian representation. Secondly, motivated by the recent success of multiple cues pedestrian detection in visual imagery, we combine both shape and binary pattern texture features for effective infrared pedestrian description, providing a level of robustness to variations in pedestrian shape and appearance in infrared images. Finally, a support vector machine (SVM) classifier is utilized to classify sub-windows into pedestrians or background. Experimental results demonstrate the robustness and effectiveness of our method.

Citation Download Citation

Hao Cui, Biao Li, and Zhenkang Shen "Combining shape and texture features for infrared pedestrian detection", Proc. SPIE 8002, MIPPR 2011: Multispectral Image Acquisition, Processing, and Analysis, 80021D (8 December 2011); https://doi.org/10.1117/12.902013

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KEYWORDS

Infrared radiation

Infrared imaging

Infrared detectors

Binary data

Surveillance

Visualization

Thermography

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