Real-Time Infrared Pedestrian Detection via Sparse Representation
This paper presents a simple,novel,yet very powerful approach for real-time infrared pedestrian detection based on random projection.In our framework,firstly,a feature-centric efficient sliding window scheme is proposed for candidate pedestrians searching.Different from the traditional threshold or edge based region of interest (ROI) generation techniques,it performs robustly under different scenes without delicate parameter tuning.Secondly,at the feature extraction stage,a small set of random features is extracted from local image patches.To the best of our knowledge,this paper is the first to investigate random projection (RP) for infrared pedestrian feature representation.Finally,the random features in a pyramid grid are concatenated to perform sub-image classification using a support vector machine (SVM) classifier.In our case,both learning and classification are carried out in a compressed domain.Experimental results in various scenarios demonstrate the robustness and effectiveness of our method.
sparse representation pedestrian detection infrared imagery random projection
Huanxin Zou Hao Sun Kefeng Ji
School of Electronic Science and Engineering National University of Defense Technology Changsha, PR China
国际会议
厦门
英文
1-4
2012-12-16（万方平台首次上网日期，不代表论文的发表时间）