Mask-Guided Attention Network for Occluded Pedestrian Detection

TL;DR

This paper introduces a mask-guided attention network that improves occluded pedestrian detection by focusing on visible regions, achieving state-of-the-art results on CityPersons and Caltech datasets.

Contribution

The paper proposes a novel attention network that emphasizes visible pedestrian regions and demonstrates coarse segmentation annotations are sufficient for training.

Findings

Achieves 9.5% improvement in log-average miss rate on CityPersons dataset.

Achieves 5.0% improvement on Caltech dataset.

Sets new state-of-the-art performance on heavily occluded pedestrian detection.

Abstract

Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance on heavily occluded pedestrians remains far from satisfactory. The main culprits are intra-class occlusions involving other pedestrians and inter-class occlusions caused by other objects, such as cars and bicycles. These result in a multitude of occlusion patterns. We propose an approach for occluded pedestrian detection with the following contributions. First, we introduce a novel mask-guided attention network that fits naturally into popular pedestrian detection pipelines. Our attention network emphasizes on visible pedestrian regions while suppressing the occluded ones by modulating full body features. Second, we empirically demonstrate that coarse-level segmentation annotations provide reasonable approximation to their dense pixel-wise counterparts. Experiments are performed on CityPersons and Caltech datasets. Our approach sets a new state-of-the-art on both datasets. Our approach obtains an absolute gain of 9.5% in log-average miss rate, compared to the best reported results on the heavily occluded (HO) pedestrian set of CityPersons test set. Further, on the HO pedestrian set of Caltech dataset, our method achieves an absolute gain of 5.0% in log-average miss rate, compared to the best reported results. Code and models are available at: https://github.com/Leotju/MGAN.