Mixture of gaussians model for robust pedestrian images detection

Automated pedestrian detection is a forward looking challenge for future driver support systems in automotive industry. Such system would have to make safety critical decisions based on poor quality images shot in real-time from the unstable moving vehicles. The proposed system offers a simple yet very effective detection methodology based on mixture of Gaussians (MoG) aided by an Expectation-Maximisation (EM) clustering algorithm. The algorithm operates on a number of features built by aggregation of different variations of the first and second order pixel gradients related to the aggregated templates of pedestrian and non-pedestrian classes. For each class the algorithm fits a fixed number of clusters and using Gaussian kernels optimises the parameters of the Gaussian Mixture model such that the probabilities of belonging to the intra-class clusters is maximised. Given a new image the system instantly generates relative features and uses mixture model to build posterior probability densities for all clusters and after aggregation and renormalisation, posterior class probabilities. The system has been fine-tuned against its parameters and feature subsets and tested using almost 10000 real images provided by DaimlerChrysler. Reaching the testing performance in excess of 95% the model was announced the winner of the NISIS Competition 2007.