Based on a sample of 8000 manually segmented video images acquired in a subway station, we examine if heads of pedestrians can be detected by use of wavelet features and parameter free statistical classifiers. In particular we address the question of which wavelet filter to use for feature generation and how to model class conditional densities of features. We show that a markov chain based approach in combination with HAAR-wavelets is a good compromise between high recognition rates and computational efficiency. The latter is required since developed algorithms are supposed to run on a dedicated low cost hardware.
['Dorothea Faulhaber', 'Heinrich Niemann', 'Peter Weierich']
Detection of Crowds of People by Use of Wavelet Features and Parameter Free Statistical Models.