Graininess-Aware Deep Feature Learning for Pedestrian Detection

Chunze Lin, Jiwen Lu, Gang Wang, Jie Zhou; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 732-747


In this paper, we propose a graininess-aware deep feature learning method for pedestrian detection. Unlike most existing pedestrian detection methods which only consider low resolution feature maps, we incorporate fine-grained information into convolutional features to make them more discriminative for human body parts. Specifically, we propose a pedestrian attention mechanism which efficiently identifies pedestrian regions. Our method encodes fine-grained attention masks into convolutional feature maps, which significantly suppresses background interference and highlights pedestrians. Hence, our graininess-aware features become more focused on pedestrians, in particular those of small size and with occlusion. We further introduce a zoom-in-zoom-out module, which enhances the features by incorporating local details and context information. We integrate these two modules into a deep neural network, forming an end-to-end trainable pedestrian detector. Comprehensive experimental results on four challenging pedestrian benchmarks demonstrate the effectiveness of the proposed approach.

Related Material

author = {Lin, Chunze and Lu, Jiwen and Wang, Gang and Zhou, Jie},
title = {Graininess-Aware Deep Feature Learning for Pedestrian Detection},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}