Design and implementation of Pedestrian detection based on Zynq APSoc
DOI:
https://doi.org/10.54097/fcis.v2i2.4087Keywords:
APSoc, Zynq, Pedestrian recognition, Soft and hard co-designAbstract
Pedestrian recognition is an important content of intelligent security surveillance video processing system. In order to meet the application requirements of miniaturization and real-time security system, a moving target recognition system based on APSoc platform is designed. The system uses ov5640 camera as the video collector, zynq7020 as the development platform, and uses the soft and hard co-design to realize each function module. Finally, the pedestrian image acquisition and real-time display system is successfully completed. The test results show that the system can effectively and stably track pedestrians and moving objects within a certain range of distance, and can be displayed in real time, miniaturized and low power consumption, which can be further applied in the field of security.
Downloads
References
Bobick A F, Andy Wilson. Using configuration states for the representation and recognition of gestures. MIT Media Lab Perceptual Computing Section Technical Report, No. 308, 1995.
Bobick A F, Wilson A D. A state-based approach to the representation and recognition of gesture [J] .IEEE Trans PAMI, 1997, 19(12): 1325-1337.
Maher M W, Marais M L. A Field Study on the Limitations of Activity-Based Costing When Resources are Provided on a Joint and Indivisible Basis[J]. Journal of Accounting Research, 1998, 36(1):129-142.
Wei S E, Ramakrishna V, Kanade T, et al. Convolutional Pose Machines[J]. 2016:4724-4732.
He, K., Gkioxari, G., Doll´ar, P., Girshick, R.: Mask R-CNN[C]. International Conference on Computer Vision, 2017: 2961-2969.
Fang, H., Xie, S., Tai, Y., Lu, C. RMPE: Regional Multi-Person Pose Estimation[C]. International Conference on Computer Vision,2017:2334-2343.
Yilun C., Zhicheng W., Yuxiang P., Zhiqiang Z. Cascaded Pyramid Network for Multi-Person Pose Estimation[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2018:7103-7111.
Ren S, Girshick R, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137-1149.
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y. Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields[C]. IEEE Conference on Computer Vision and Pattern Recognition,2017:7291-7299.
Newell, A., Huang, Z., Deng, J. Associative Embedding: End-to-End Learning for Joint Detection and Grouping[C]. Neural Information Processing Systems, 2017.


