Abstract
This paper proposes an improved pedestrian detection algorithm based on SSD (Single Shot MultiBox Detector) model. The algorithm mainly solves the problem that the detection effect on small-scale pedestrians is not ideal when the SSD pedestrian detection algorithm performs the pedestrian detection task. The algorithm improves the original SSD model by introducing a shallower feature map. The pedestrian detection is carried out by using the characteristics of different output layers in the model, and the multi-layer detection results are combined to improve the detection effect of small-scale pedestrian. The Squeeze-and-Excitation Module is introduced in the additional feature layer of the SSD model. The improved model can automatically acquire the importance of each channel by self-learning, according to which useful features can be enhanced and features that are not useful for current tasks can be suppressed, thereby further improving the detection ability of the algorithm for small-scale pedestrians. Experiments show that the accuracy of the proposed algorithm in the INRIA dataset reaches 93.17%, and the missed detection rate is as low as 8.72%.
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References
Pal, G., Rudrapaul, D., Acharjee, S.: Video shot boundary detection: a review. In: Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India CSI, vol. 2, pp. 119–127. Springer, Cham (2015)
Li, X.D., Ye, M., Li, T.: Summary of research on target detection based on convolution neural network. Comput. Appl. Res. 34(10), 2881–2886+2891 (2017)
Benenson, R., Omran, M., Hosang, J., Schiele, B.: Ten years of pedestrian detection, what have we learned? In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 613–627. Springer, Cham (2015)
Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. Int. J. Comput. Vision 63(2), 153–161 (2005)
Felzenszwalb, P., Girshick, R.B., McAllester, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2009)
Dollr, P., Tu, Z., Perona, P.: Integral channel features. In: British Machine Vision Conference, pp. 91.1–91.11, London (2009)
Nam, W., Dollár, P., Han, J.H.: Local decorrelation for improved pedestrian detection. In: Advances in Neural Information Processing Systems, pp. 424–432 (2014)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Girshick R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
Ren, S., He, K., Girshick, R.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779–788 (2016)
Liu, W., Anguelov, D., Erhan, D.: SSD: single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37. Springer, Cham (2016)
Oliva, A., Torralba, A.: The role of context in object recognition. Trends Cogn. Sci. 11(12), 520–527 (2007)
Divvala, S., Hoiem, D., Hays, J.: An empirical study of context in object detection. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE press, Miami, pp. 1271–1278 (2009)
Fang, S., Li, Y., Liu, X., Fan, D.: An improved multi-scale face detection algorithm based on SSD model. Inf. Technol. Inf. 02, 39–42 (2019)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
INRIA Person Dataset.: http://pascal.inrialpes.fr/data/human/. Accessed 19 Sep 2018
Wu, P.Y., Zhang, J.M., Peng, J., Lu, C.Q.: Research on man detection in real scene with multilayer convolutional characteristics. J. Intell. Syst. 02, 306–315 (2019)
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Fan, D., Liu, D., Chi, W., Liu, X., Li, Y. (2020). Improved SSD-Based Multi-scale Pedestrian Detection Algorithm. In: Kountchev, R., Patnaik, S., Shi, J., Favorskaya, M. (eds) Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. Smart Innovation, Systems and Technologies, vol 180. Springer, Singapore. https://doi.org/10.1007/978-981-15-3867-4_14
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DOI: https://doi.org/10.1007/978-981-15-3867-4_14
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