Feature subset selection based on co-evolution for pedestrian detection

An appropriate subset of features is needed for a classification-based pedestrian detection system since its performance is greatly affected by the features adopted. Moreover, the combination of different types of features (eg, grey-scale, colour) could improve the detection accuracy, so it is helpful to obtain a feature subset and the proportion of each type simultaneously for the classifier. However, because a larger number and various types of features are generally extracted to represent pedestrians better, it is difficult to achieve this. This paper proposed a co-evolutionary method to solve this problem. In the feature subset selection method, each sub-population mapped to one type of pedestrian feature, and then all sub-populations evolved co-operatively to obtain an optimal feature subset. Moreover, a strategy was specially designed to adjust the sub-population size adaptively in order to improve the optimizing performance. The proposed method has been tested on pedestrian detection applications and the experimental results illustrate its better performance compared with other methods such as genetic algorithm and AdaBoost.