Fast pedestrian detection using multimodal estimation of distribution algorithms

Pedestrian detection plays a pivotal role in various domains but is still a challenging problem nowadays. In this study, we transform the multiple-pedestrian detection problem into a multimodal optimization problem and then utilize a multimodal estimation of distribution algorithm (MEDA) to optimize this problem based on Histograms of Oriented Gradients (HOG) feature and Support Vector Machines (SVM). Specifically, we adopt a three-dimensional vector to represent a rectangular region of an image and also use it to encode individuals. Then, a state-of-the-art multimodal optimization algorithm called MEDA is utilized to evolve the individuals, so that a series of optimal rectangular regions containing pedestrians can be obtained. Experiments conducted on a set of images from one pedestrian dataset called INRIA confirm that in comparison with the classical HOG-SVM method and one state-of-the-art method, the developed algorithm cannot only achieve higher detection accuracy on images containing different numbers of pedestrians, but also can remain high computational efficiency.