標題: Fast Deformable Model for Pedestrian Detection with Haar-like Features
作者: Chou, Kuang-Pen
Prasad, Mukesh
Puthal, Deepak
Chen, Ping-Hung
Vishwakarma, Dinesh Kumar
Sundaram, Suresh
Lin, Chin-Teng
Lin, Wen-Chieh
資訊工程學系
電控工程研究所
Department of Computer Science
Institute of Electrical and Control Engineering
關鍵字: Pedestrian;Adaboost;Multi-view;Deformable part model
公開日期: 1-Jan-2017
摘要: This paper proposes a novel Fast Deformable Model for Pedestrian Detection (FDMPD) to detect the pedestrians efficiently and accurately in the crowded environment. Despite of multiple detection methods available, detection becomes difficult due to variety of human postures and perspectives. The proposed study is divided into two parts. First part trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. Second part uses these six response feature maps with full-body model to produce spatial deep features. The combined deep features are used as an input to SVM to judge the existence of pedestrian. As per the experiments conducted on the INRIA person dataset, the proposed FDMPD approach shows greater than 44.75 % improvement compared to other state-of-the-art methods in terms of efficiency and robustness.
URI: http://hdl.handle.net/11536/147211
期刊: 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI)
起始頁: 259
結束頁: 266
Appears in Collections:Conferences Paper