期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 29, 期 -, 页码 3820-3834出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2020.2966371
关键词
Pedestrian detection; attention; deep learning; graininess
资金
- National Key Research and Development Program of China [2016YFB1001001]
- National Natural Science Foundation of China [61822603, U1813218, U1713214, 61672306]
- Shenzhen Fundamental Research Fund (Subject Arrangement) [JCYJ20170412170602564]
- Zhejiang Leading Innovation Research Program [2018R01017]
In this paper, we propose a graininess-aware deep feature learning method for pedestrian detection. Unlike most existing methods which utilize the convolutional features without explicit distinction, we appropriately exploit multiple convolutional layers and dynamically select most informative features. Specifically, we train a multi-scale pedestrian attention via pixel-wise segmentation supervision to efficiently identify the pedestrian of particular scales. We encodes the fine-grained attention map into the feature maps of the detection layers to guide them to highlight the pedestrians of specific scale and avoid the background interference. The graininess-aware feature maps generated with our attention mechanism are more focused on pedestrians, and in particular on the small-scale and occluded targets. We further introduce a zoom-in-zoom-out module to enhances the features by incorporating local details and context information. Extensive experimental results on five challenging pedestrian detection benchmarks show that our method achieves very competitive or even better performance with the state-of-the-arts and is faster than most existing approaches.
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