Abstract
We present an algorithm for the on-board vision vehicle detection problem using a cascade of boosted classifiers. Three families of features are compared: the rectangular filters (Haar-like features), the histograms of oriented gradient (HoG), and their combination (a concatenation of the two preceding features). A comparative study of the results of the generative (HoG features), discriminative (Haar-like features) detectors, and of their fusion is presented. These results show that the fusion combines the advantages of the other two detectors: generative classifiers eliminate "easily" negative examples in the early layers of the cascade, while in the later layers, the discriminative classifiers generate a fine decision boundary removing the negative examples near the vehicle model. The best algorithm achieves good performances on a test set containing some 500 vehicle images: the detection rate is about 94% and the false-alarm rate per image is 0.0003.
Highlights
The increasing number of cars has increased the demand of driver assistance systems which makes driving more comfortable and safe [1]
The performance measures are the correct detections rate corresponding to the ratio of correct detections to the total number of vehicles present in the test database: the false-alarm rate computed as the average number of false alarms per image divided by the total number of windows evaluated by the detector in an image
Comparing the false-alarm rate, Haar-like features are more discriminative than histograms of oriented gradient (HoG) features
Summary
The increasing number of cars has increased the demand of driver assistance systems which makes driving more comfortable and safe [1]. Many researches have been conducted by the intelligent transport systems (ITSs) community in this field. It deals with the installation of high-tech devices and other controllers on vehicles and road networks. Among the systems to be integrated on intelligent vehicles, it is necessary to distinguish those related to perception. They can be either proprioceptive (to deal with the vehicle internal state) or exteroceptive (to deal with the vehicle external environment). In this framework, many vision-based sensors are being studied now. An on-board vision system can provide information about the localization and the size of other vehicles in the environment, the road, the traffic signs, and the other users of the road network
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