A vehicle detection approach based on multi-features fusion in the fisheye images

Cheng, Guoxiang; Xue, Chengqi

doi:10.1109/iccrd.2011.5763840

Cited by 8 publications

(2 citation statements)

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“…By applying these two filters, orientation and magnitude values of gradients for each pixel are obtained. In orientation binning step, the orientation bins are evenly divided into the predefined range, e.g., 6 bins with π/3 range. After the number of the orientation bins is determined, a histogram is generated by accumulating a magnitude value for each pixel of an image in the histogram generation step.…”

Section: Featurementioning

confidence: 99%

“…The HG step generally uses one of the following three methods: the knowledge-based method, the stereo vision-based method or the motion-based method. To detect objects and hypothesizes the location of object, the knowledge-based method uses characteristic information such as symmetry [3][4][5][6], corners [7,8], shadows [9], edges [10][11][12], textures [13,14], and vehicle lights [15][16][17]. There are two types of the stereo vision-based method for detecting vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Vision-Based Vehicle Detection System Applying Hypothesis Fitting

Cheon¹,

Lee²

2017

IJFIS

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In this paper, we propose the improved vision-based vehicle detection system which is added a hypothesis fitting (HF) step to the typical vehicle detection system consisting of the hypothesis generation (HG) and hypothesis verification (HV) step. In the HG step, the system generates hypotheses using shadow regions appearing under vehicles. In the HV step, the system verifies whether a hypothesis is a vehicle or not by applying a classifier and feature vectors extracted from the hypothesis. The proposed HF step is conducted between HG and HV steps and help to improve performance of the vehicle verification by adapting region of hypotheses. We verify the performance of our proposed HF method through a total of 4,797 hypotheses, 1,606 positive hypotheses, and 3,191 negative hypotheses data set.

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Section: Featurementioning

confidence: 99%