On Integration of Features and Classifiers for Robust Vehicle Detection

Some researches have demonstrated that a single recognition system is not usually able to deal with the diversity of environment situations in images. In this paper, with the aim of finding a robust method to compensate single classifier inability under certain circumstances, an extensive study on how to combine features and classifiers is performed. Two ways of integrating features and classifiers are proposed: concatenated vector and ensemble architecture. These two methods are composed by Histogram of Oriented Gradients and Local Receptive Fields as feature extractors, and a Multi Layer Perceptron and Support Vector Machines as classifiers. A thorough analysis with respect to the robustness of the proposed methods over artificial illumination changing has been experimentally carried out at a front and rear vehicle recognition task. Results have demonstrated that the ensemble architecture with a heuristic Majority Voting presented the best performance (other four classification fusion methods based on majority voting and fuzzy integral were also evaluated). The ensemble classifier obtained an average hit rate of 92.4% and less than 1% of false alarm rate under multiple datasets and environment conditions.