Mixture of Gaussians Model for Robust Pedestrian Images Detection

TL;DRAbstract

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 intraclass clusters is maximised. Given a new image the system instantly generates relative features and uses mixture model to build posterior probability de

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