Abstract
All kinds of vehicles have different ratios of width to height, which are called the aspect ratios. Most previous works, however, use a fixed aspect ratio for vehicle detection (VD). The use of a fixed vehicle aspect ratio for VD degrades the performance. Thus, the estimation of a vehicle aspect ratio is an important part of robust VD. Taking this idea into account, a new on-road vehicle detection system is proposed in this paper. The proposed method estimates the aspect ratio of the hypothesized windows to improve the VD performance. Our proposed method uses an Aggregate Channel Feature (ACF) and a support vector machine (SVM) to verify the hypothesized windows with the estimated aspect ratio. The contribution of this paper is threefold. First, the estimation of vehicle aspect ratio is inserted between the HG (hypothesis generation) and the HV (hypothesis verification). Second, a simple HG method named a signed horizontal edge map is proposed to speed up VD. Third, a new measure is proposed to represent the overlapping ratio between the ground truth and the detection results. This new measure is used to show that the proposed method is better than previous works in terms of robust VD. Finally, the Pittsburgh dataset is used to verify the performance of the proposed method.
Highlights
Vehicle detection (VD) is one of the major research issues within intelligent transportation system (ITS) organizations, and considerable research has been conducted
In situations thatInretqhuisirpeatpheerv, eahpicrleecipsoesniteiownoann-drosaidzev, eahcciculreadteetveechtiiocnlesdyestteecmtiohnasisbveeernypimroppoorsteadn.t.InFsoirtuaactciuornaste thvaethriecqleudireetethcteiovne,htihclee spigonsietdionhoarnizdosnitzael, eadccguermataepvewhaicsleprdoepteocsteidoninisthveerHy Gimapnodrttahnet.aFsoprecatccruatriaoteof vehicle detection, the signed horizontal edge map was proposed in the Hypothesis Generation (HG) and the aspect ratio of the vehicle windows was estimated in the HI
The windows from the HI were provided to the HV composed of the Aggregate Channel Feature (ACF) and support vector machine (SVM), and good VD performance was obtained
Summary
Vehicle detection (VD) is one of the major research issues within intelligent transportation system (ITS) organizations, and considerable research has been conducted. In [9], the linear model between the vehicle position and vehicle size is updated using a recursive least square algorithm This linear model helps to generate the Region of interests (ROIs) such that they are likely to include vehicle regions. This approach can reduce false positives as compared with the previous exhaustive search or sliding window approaches. The detection performance using the Haar-like wavelet is lower than that of the HOG or Gabor feature. In [17], the HOG and Haar-like wavelet are combined in cascade form to reduce the computational time and to improve the detection performance.
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