Evaluation of Optimum Path Forest Classifier for Pedestrian Detection

Wendell Fioravante Silva Diniz (1, 2) , Vincent Frémont (3, 4) , Isabelle Fantoni (3, 4) , Euripedes Nobrega (1, 2)

1 UNICAMP - Universidade Estadual de Campinas = University of Campinas
2 UNICAMP - Departamento Mecanica Computacional Faculdade de Engenharia Mecânica
3 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
4 Labex MS2T - Laboratoire d'Excellence "Maîtrise des Systèmes de Systèmes Technologiques"

Machine learning (ML) and image processing techniques have been applied together to various scenarios for the development of Intelligent Vehicles. Among these scenarios, pedestrian detection has received growing interest in recent years, since high concern for safety applications in traffic has arisen. Several ML methods were successfully applied to solve this problem. However, because pedestrian detection is in general computationally intensive, a good trade off between accuracy and processing time is desirable, particularly if the methods are directed to real-time applications. Optimum Path Forest (OPF) classifier is a recently developed non-parametric classifier method. This work contribution is the performance assessment of a novel OPF application to pedestrian detection. Results have shown that it is fast and competitive against established methods and a viable alternative to be considered for machine learning and pedestrian detection applications.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Robotique [cs.RO]

Wendell Fioravante Silva Diniz, Vincent Frémont, Isabelle Fantoni, Euripedes Nobrega. Evaluation of Optimum Path Forest Classifier for Pedestrian Detection. IEEE Conference on Robotics and Biomimetics (ROBIO 2015), Dec 2015, Zhuhai, China. pp.899-904. ⟨hal-01260496⟩