On-road vehicle detection based on effective hypothesis generation
RO-MAN（2013）
摘要
This paper proposes an effective hypothesis generation for detection multi-vehicle using a monocular camera fixed on the host vehicle. In hypothesis generation (HG) step, we use linear model between the distance and vehicle size by using recursive least square. It generates effective image patches and improves the detection performance. In addition, it also reduces the computation time compared with sliding-window approach. In hypothesis verification (HV) step, we use the Histogram of Oriented Gradient (HOG) feature and Support Vector Machine (SVM). In our experiment, Caltech and IR datasets are used. The experimental result shows the improvement of running time and detection performance.
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关键词
detection performance,on-road vehicle detection,recursive least square,vehicle size,linear model,road vehicles,monocular camera,caltech datasets,traffic engineering computing,ir datasets,running time,svm,hypothesis verification step,computation time,least squares approximations,support vector machine,hv step,sliding-window approach,gradient methods,object detection,host vehicle,hg step,histogram of oriented gradient feature,recursive estimation,effective hypothesis generation,image patches,support vector machines,multi-vehicle detection,hog feature
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