# Exploring Human Vision Driven Features for Pedestrian Detection

arXiv: https://arxiv.org/abs/1501.06180 · HTML: https://mlsanity.com/paper/1501.06180/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2015.

## Built on by (most cited first, 3 of 3)

- [MS-DETR: Multispectral Pedestrian Detection Transformer with Loosely Coupled Fusion and Modality-Balanced Optimization](https://mlsanity.com/paper/2302.00290/) (2023)
- [Integrating Language-Derived Appearance Elements with Visual Cues in Pedestrian Detection](https://mlsanity.com/paper/2311.01025/) (2023)
- [Efficient Learning of Pinball TWSVM using Privileged Information and its applications](https://mlsanity.com/paper/2107.06744/) (2021)

## Abstract (arXiv)

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.