Abstract-This paper presents a monocular algorithm for front and rear vehicle detection, developed as part of the FP7 V-Charge project's perception system. The system is made of an AdaBoost classifier with Haar Features Decision Stump. It processes several virtual perspective images, obtained by unwarping 4 monocular fish-eye cameras mounted all-around an autonomous electric car. The target scenario is the automated valet parking, but the presented technique fits well in any general urban and highway environment. A great attention has been given to optimize the computational performance. The accuracy in the detection and a low computation costs are provided by combining a multiscale detection scheme with a Soft-Cascade classifier design. The algorithm runs in real time on the project's hardware platform. The system has been tested on a validation set, compared with several AdaBoost schemes, and the corresponding results and statistics are also reported.
AdaBoost classifier with Haar feature decision stumps were used for detection of vehicles for parking at early stages REF .
Vehicle detection for autonomous parking using a Soft-Cascade AdaBoost classifier
2014 IEEE Intelligent Vehicles Symposium Proceedings
2014 IEEE Intelligent Vehicles Symposium Proceedings
Engineering Computer Science