An Evaluation of the Pedestrian Classification in a Multi-Domain Multi-Modality Setup

A Miron (1) , A Rogozan (2, 3) , S Ainouz (3) , A B Bensrhair (3) , A B Broggi (4)

1 ISR - Intelligent Systems Research Laboratory
2 INSA Rouen Normandie - Institut national des sciences appliquées Rouen Normandie
3 STI - LITIS - Equipe Systèmes de Transport Intelligent
4 Department of Information Engineering

The objective of this article is to study the problem of pedestrian classification across different light spectrum domains (visible and far-infrared (FIR)) and modalities (intensity, depth and motion). In recent years, there has been a number of approaches for classifying and detecting pedestrians in both FIR and visible images, but the methods are difficult to compare, because either the datasets are not publicly available or they do not offer a comparison between the two domains. Our two primary contributions are the following: (1) we propose a public dataset, named RIFIR , containing both FIR and visible images collected in an urban environment from a moving vehicle during daytime; and (2) we compare the state-of-the-art features in a multi-modality setup: intensity, depth and flow, in far-infrared over visible domains. The experiments show that features families, intensity self-similarity (ISS), local binary patterns (LBP), local gradient patterns (LGP) and histogram of oriented gradients (HOG), computed from FIR and visible domains are highly complementary, but their relative performance varies across different modalities. In our experiments, the FIR domain has proven superior to the visible one for the task of pedestrian classification, but the overall best results are obtained by a multi-domain multi-modality multi-feature fusion. Sensors 2015, 15 13852

Mots clés: stereovision; feature comparison; intensity self-similarity; benchmark; multi-cue; multi-modality; multi-domain; infrared pedestrian classification

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

A Miron, A Rogozan, S Ainouz, A B Bensrhair, A B Broggi. An Evaluation of the Pedestrian Classification in a Multi-Domain Multi-Modality Setup. Sensors, 2015, 15 (6), pp.13851-13873. ⟨10.3390/s150613851⟩. ⟨hal-01535904⟩