Detection of front-view vehicle with occlusions using AdaBoost - Details

Author：

Wu, Chunpeng (Wu, Chunpeng.) | Duan, Lijuan (Duan, Lijuan.) (Scholars：段立娟) | Miao, Jun (Miao, Jun.) | Fang, Faming (Fang, Faming.) | Wang, Xuebin (Wang, Xuebin.)

Indexed by：

EI Scopus

Abstract：

In　this　paper,　we　propose　a　vehicle　detection　method　based　on　AdaBoost.　We　focus　on　the　detection　of　front-view　car　and　bus　with　occlusions　on　highway.　Samples　with　different　occlusion　situations　are　selected　into　the　training　set.　By　using　basic　and　rotated　Haar-like　features　extracted　from　the　samples　in　the　set,　we　train　an　AdaBoost-based　cascade　vehicle　detector.　The　performance　tests　on　static　images　and　short　time　videos　show　that　(1)our　approach　detects　cars　more　effectively　than　buses　(2)the　real-time　detection　of　our　method　on　video　proceeds　at　30　frames　per　second.　©2009　IEEE.

Keyword：

Feature extraction Vehicles Adaptive boosting Sampling

Author Community：

[ 1 ] [Wu, Chunpeng]College of Computer Sciences, Beijing University of Technology, Beijing 100124, China
[ 2 ] [Duan, Lijuan]College of Computer Sciences, Beijing University of Technology, Beijing 100124, China
[ 3 ] [Miao, Jun]Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, China Academy of Sciences, Beijing 100190, China
[ 4 ] [Fang, Faming]College of Computer Sciences, Beijing University of Technology, Beijing 100124, China
[ 5 ] [Wang, Xuebin]College of Computer Sciences, Beijing University of Technology, Beijing 100124, China