This paper presents a monocular machine vision system capable of detecting vehicles in front or behind of our own vehicle. The system consists of two main steps: 1) generation of candidates with respect to a vehicle by analyzing textures, 2) verification of the candidates by an appearance-based method using the AdaBoost learning algorithm. The vehicle candidates are generated by exploiting the facts that a vehicle has vertical and horizontal lines, and furthermore the rear and frontal shapes of a vehicle show symmetry. The proposed system is proven to be effective through experiments under various traffic scenarios.
Vehicle detection by edge-based candidate generation and appearance-based classification
2008 IEEE Intelligent Vehicles Symposium ; 428-433
2008-06-01
741334 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Vehicle Detection by Edge-Based Candidate Generation and Appearance-Based Classification
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