Exploring Human Vision Driven Features for Pedestrian Detection

TL;DR

This paper introduces a human vision-inspired feature extraction method using contrast maps and multi-scale analysis for pedestrian detection, achieving state-of-the-art results on benchmark datasets.

Contribution

It proposes a novel multi-channel, multi-scale contrast descriptor inspired by human visual attention, with extensive evaluation demonstrating its effectiveness.

Findings

Achieves state-of-the-art pedestrian detection accuracy.

Effective feature selection through extensive comparison.

Robust performance across different datasets.

Abstract

Motivated by the center-surround mechanism in the human visual attention system, we propose to use average contrast maps for the challenge of pedestrian detection in street scenes due to the observation that pedestrians indeed exhibit discriminative contrast texture. Our main contributions are first to design a local, statistical multi-channel descriptorin order to incorporate both color and gradient information. Second, we introduce a multi-direction and multi-scale contrast scheme based on grid-cells in order to integrate expressive local variations. Contributing to the issue of selecting most discriminative features for assessing and classification, we perform extensive comparisons w.r.t. statistical descriptors, contrast measurements, and scale structures. This way, we obtain reasonable results under various configurations. Empirical findings from applying our optimized detector on the INRIA and Caltech pedestrian datasets show that our features yield state-of-the-art performance in pedestrian detection.