Exploring Human Vision Driven Features for Pedestrian Detection
This work addresses pedestrian detection for autonomous driving and surveillance, but it appears incremental as it builds on existing contrast-based methods.
The paper tackled pedestrian detection in street scenes by proposing features inspired by human vision's center-surround mechanism, achieving state-of-the-art performance on INRIA and Caltech datasets.
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.