Evolutionary Optimization ofWavelet Feature Sets for Real-Time Pedestrian Classification

Computer vision for object detection often relies on complex classifiers and large feature sets to achieve high detection rates. But when real-time constraints have to be met, for example in driver assistance systems, fast classifiers are required. Here we consider the design of a computationally efficient system for pedestrian detection. We propose an evolutionary algorithm for the optimization of a small set of wavelet features, which can be computed very efficiently. These features serve as input to a linear classifier. The classification performance of the optimized system is on par with recently published results obtained with support vector machines on large feature sets, while the computational time is lower by orders of magnitude.