Pedestrian Detection in Driver Assistance Using SSD and PS-GAN

Kun Zheng, Mengfei Wei, Shenhui Li, Dong Yang, Xudong Liu


Pedestrian detection is a critical challenge in the field of general object detection, the performance of object detection has advanced with the development of deep learning. However, considerable improvement is still required for pedestrian detection, considering the differences in pedestrian wears, action, and posture. In the driver assistance system, it is necessary to further improve the intelligent pedestrian detection ability. We present a method based on the combination of SSD and GAN to improve the performance of pedestrian detection. Firstly, we assess the impact of different kinds of methods which can detect pedestrians based on SSD and optimize the detection for pedestrian characteristics. Secondly, we propose a novel network architecture, namely data synthesis PS-GAN to generate diverse pedestrian data for verifying the effectiveness of massive training data to SSD detector. Experimental results show that the proposed manners can improve the performance of pedestrian detection to some extent. At last, we use the pedestrian detector to simulate a specific application of motor vehicle assisted driving which would make the detector focus on specific pedestrians according to the velocity of the vehicle. The results establish the validity of the approach.


Pedestrian Detection; Driver Assistance; GAN; SSD

Full Text:



1. Dollar P, Wojek C, Schiele B, et al. Pedestrian detection: a benchmark. Proc. conf. on Computer Vision & Pattern Recognition, 304-311, 2009.

2. Piotr Dollár, Tu Z, Perona P, et al. Integral channel features. British Machine Vision Conference. DBLP 2009.

3. Urtasun R, Lenz P, Geiger A. Are we ready for autonomous driving? The KITTI vision benchmark suite. IEEE Conference on Computer Vision & Pattern Recognition 2012.

4. Benenson R, Omran M, Hosang J, et al. Ten years of pedestrian detection, what have we learned?2014.

5. Zhang L, Lin L, Liang X, et al. Is faster r-cnn doing well for pedestrian detection? 2016.

6. Kakadiaris IA, Metaxas D. 3D human body model acquisition from multiple views. International Conference on Computer Vision. IEEE 1995.

7. Rohr K. Towards model-based recognition of human movements in image sequences. CVGIP: Image Understanding 1994; 59(1): 94-115.

8. Dalal N, Triggs B. Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision & Pattern Recognition 2005.

9. Sermanet P, Kavukcuoglu K, Chintala S, et al. Pedestrian detection with unsupervised multi-stage feature learning. Computer Vision & Pattern Recognition 2013.

10. Ye Q, Jiao J, Zhang B. Fast pedestrian detection with multi-scale orientation features and two-stage classifiers. IEEE International Conference on Image Processing. IEEE 2010.

11. Bilgic B, Horn BKP, Masaki I. Fast human detection with cascaded ensembles on the GPU. Intelligent Vehicles Symposium 2010.

12. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. International Conference on Neural Information Processing Systems. Curran Associates Inc 2012.

13. Russakovsky O, Deng J, Su H, et al. Imagenet large scale visual recognition challenge. International Journal of Computer Vision 2014; 115(3).

14. Sermanet P, Kavukcuoglu K, Chintala S, et al. Pedestrian detection with unsupervised multi-stage feature learning. In CVPR 2013; 1, 2, 5.

15. Felzenszwalb P, Mcallester D, Ramanan D. A discriminatively trained, multiscale, deformable part model. Cvpr 2008; 8: 1-8.

16. Felzenszwalb PF, Girshick RB, Mcallester D, et al. Object detection with discriminatively trained part-based models. IEEE Transactions on Software Engineering 2010; 32(9): 1627-1645.

17. Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society.

18. Tian Y, Ping L, Wang X, et al. Deep learning strong parts for pedestrian detection. IEEE International Conference on Computer Vision 2016.

19. Tian Y, Ping L, Wang X, et al. Pedestrian detection aided by deep learning semantic tasks 2015.

20. Nam W, Dollar P, Han JH. Local decorrelation for improved pedestrian detection. NIPS 2014.

21. Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector 2015.

22. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Computer Science 2014.

23. Wojek C, Dollar P, Schiele B, et al. Pedestrian detection: an evaluation of the state of the art. IEEE Transactions on Pattern Analysis & Machine Intelligence 2012.

24. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition 2015.

25. Shrivastava A, Gupta A, Girshick R. Training region-based object detectors with online hard example mining 2016.

26. Lin TY, Goyal P, Girshick R, et al. Focal loss for dense object detection. IEEE Transactions on Pattern Analysis & Machine Intelligence 2017; PP(99), 2999-3007.

27. Schilling MF, Watkins AE, Watkins W. Is human height bimodal? American Statistician 2002; 56(3): 223-229.

28. Goodfellow Ian J., Pouget-Abadie Jean, Mirza Mehdi, et al. Generative Adversarial Networks 2014. eprint arXiv:1406.2661.

29. Qin Pengda, Xu Weiran, Wang William Yang. DSGAN: Generative adversarial training for distant supervision relation extraction. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics 2018; 496-505.

30. Zheng K, Wei M, Sun G, et al. Using vehicle synthesis generative adversarial networks to improve vehicle detection in remote sensing images. ISPRS Int. J. Geo-Inf. 2019; 8: 390.

31. Wei M, Zheng K, Li S, et al. The target detection based on YOLOv3 and PVSGAN. Basic& Clinical Pharmacology&Toxicology 2019; 074(125): 45-45.



  • There are currently no refbacks.

Copyright (c) 2019 Kun Zheng, Mengfei Wei, Shenhui Li, Dong Yang, Xudong Liu

License URL: