Single shot relation detector for pedestrian detection

Junfeng Bai; Zongqing Lu; Qingmin Liao

doi:10.1117/12.2540283

14 August 2019 Single shot relation detector for pedestrian detection

Junfeng Bai, Zongqing Lu, Qingmin Liao

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117929 (2019) https://doi.org/10.1117/12.2540283
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

Although it is well believed for years that contextual information and relation between pedestrians would help pedestrians recognition, but this idea is rarely used in the deep learning era. This is due to the fact that the convolution method of deep neural networks is not easy to fuse related features and will increase the amount of computation. In this paper, we propose a single shot proposal relation based approach for pedestrian detection. We get the proposal on the image features of different scales, and use these proposal relationships to extend the features of each proposal. Finally, the position of the pedestrian is obtained through the convolutional neural network. Its computational cost is small and it is easy to embed into existing networks. Our detector is trained in an end-to-end fashion, experimental results on the Caltech Pedestrian dataset show that our approach achieves state-of-the-art performance.

Citation Download Citation

Junfeng Bai, Zongqing Lu, and Qingmin Liao "Single shot relation detector for pedestrian detection", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117929 (14 August 2019); https://doi.org/10.1117/12.2540283

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