• Laser & Optoelectronics Progress
  • Vol. 58, Issue 6, 610009 (2021)
Geng Pengzhi, Yang Zhixiong, Zhang Jiajun, and Tang Yunqi*
Author Affiliations
  • School of Criminal investigation, People''s Public Security University of China, Beijing 100038, China
  • show less
    DOI: 10.3788/LOP202158.0610009 Cite this Article Set citation alerts
    Geng Pengzhi, Yang Zhixiong, Zhang Jiajun, Tang Yunqi. Pedestrian Shoes Detection Algorithm Based on SSD[J]. Laser & Optoelectronics Progress, 2021, 58(6): 610009 Copy Citation Text show less
    References

    [1] Sun Y H[J]. Insight on comprehensive application of various detection means into video tracking Forensic Science and Technology, 2019, 257-260.

    [2] Nong D S[J]. Based on the footprint of the scene, expand video investigation Legal System and Society, 2015, 255-256.

    [3] Yuan C P, Yu S W. A preliminary study on the application of footprint analysis in video investigation work[J]. Guangdong Public Security Science and Technology, 25, 61-63, 74(2017).

    [4] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[EB/OL]. (2016-12-29)[2020-06-07]. https://arxiv.org/abs/1512.02325.

    [5] Xu L, Li Z H, Li Z G et al[J]. A murder case investigated and solved by applying the simulation experiment into the collected video Forensic Science and Technology, 2018, 330-333.

    [6] Yang M J, Tang Y Q, Jiang X J. Novel shoe type recognition method based on convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 191505(2019).

    [7] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), June 20-25, 2005, San Diego, CA, USA., 886-893(2005).

    [8] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

    [9] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. //2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA., 580-587(2014).

    [10] He K M, Zhang X Y, Ren S Q et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916(2015). http://www.sciencedirect.com/science/article/pii/S0031320315004252

    [11] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA, 779-788(2016).

    [12] Everingham M, Gool L. Williams C K I, et al. The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 88, 303-338(2010).

    [13] Li Z X, Zhou F Q. FSSD: feature fusion single shot multibox detector[EB/OL]. (2018-05-17)[2020-06-07]. https://arxiv.org/abs/1712.00960.

    [14] Xiang W, Zhang D Q, Yu H et al. Context-aware single-shot detector[C]. //2018 IEEE Winter Conference on Applications of Computer Vision (WACV), March 12-15, 2018, Lake Tahoe, NV, USA., 1784-1793(2018).

    [15] Fu C Y, Liu W, Ranga A et al. DSSD: deconvolutional single shot detector[EB/OL]. (2017-01-23)[2020-06-07]. https://arxiv.org/abs/1701.06659.

    [16] Jeong J, Park H, Kwak N. Enhancement of SSD by concatenating feature maps for object detection[C]. //Procedings of the British Machine Vision Conference 2017, September 4-7, 2017, London, UK. Blue Mountains: British Machine Vision Association(2017).

    [17] Shen Y J, Hao Z H, Wang P F et al. A novel human detection approach based on depth map via kinect[C]. //2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, June 23-28, 2013, Portland, OR, USA., 535-541(2013).

    [18] Zhang H Y, Wang S N, Hu W B. Improved method for estimating number of people based on convolution neural network[J]. Laser & Optoelectronics Progress, 55, 121503(2018).

    [19] Ma Y J, Li X Y, Song X F. Traffic sign recognition based on improved deep convolution neural network[J]. Laser & Optoelectronics Progress, 55, 121009(2018).

    [20] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[2020-06-07]. https://arxiv.org/abs/1409.1556.

    [21] Caesar H, Uijlings J, Ferrari V. COCO-stuff: thing and stuff classes in context[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, 1209-1218(2018).

    [22] Liu W, Rabinovich A, Berg A C. Parsenet: looking wider to see better[EB/OL]. (2015-11-19)[2020-06-07]. https://arxiv.org/abs/1506.04579.

    [23] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

    [24] Cao G M, Xie X M, Yang W Z et al. Feature-fused SSD: fast detection for small objects[J]. Proceedings of SPIE, 1061, 106151E(2018). http://www.researchgate.net/publication/324423720_Feature-fused_SSD_fast_detection_for_small_objects

    [25] Liu S T, Huang D, Wang Y H. Receptive field block net for accurate and fast object detection[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11215, 404-419(2018).

    [26] Huang J, Rathod V, Sun C et al. Speed/accuracy trade-offs for modern convolutional object detectors[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 3296-3297(2017).

    [27] Tan M X, Le Q V. EfficientNet: rethinking model scaling for convolutional neural networks[EB/OL]. (2019-05-28)[2020-06-07]. https://arxiv.org/abs/1905.11946.

    [28] Howard A G, Zhu M L, Chen B et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. (2019-04-17)[2020-06-07]. https://arxiv.org/abs/1704.04861.

    Geng Pengzhi, Yang Zhixiong, Zhang Jiajun, Tang Yunqi. Pedestrian Shoes Detection Algorithm Based on SSD[J]. Laser & Optoelectronics Progress, 2021, 58(6): 610009
    Download Citation