An Efficient Solution for People Tracking and Profiling from Video Streams Using Low-Power Compute

1. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

   Google Scholar 

2. 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)

   Google Scholar 

3. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

   Google Scholar 

4. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)

   Google Scholar 

5. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

   Google Scholar 

6. 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)

   Google Scholar 

7. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)

   Google Scholar 

8. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

9. Huang, R., Pedoeem, J., Chen, C.: YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 2503–2510 (2018)

   Google Scholar 

10. Welch, G., Bishop, G.: An introduction to the Kalman filter (1995)

    Google Scholar 

11. Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464–3468 (2016)

    Google Scholar 

12. Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 3645–3649 (2017)

    Google Scholar 

13. Déniz, O., Bueno, G., Salido, J., De la Torre, F.: Face recognition using histograms of oriented gradients. Pattern Recognit. Lett. 32(12), 1598–1603 (2011)

    Article  Google Scholar 

14. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I (2001)

    Google Scholar 

15. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)

    Article  Google Scholar 

16. Qi, R., Jia, R.S., Mao, Q.C., Sun, H.M., Zuo, L.Q.: Face detection method based on cascaded convolutional networks. IEEE Access 7, 110740–110748 (2019)

    Article  Google Scholar 

17. Xiang, Y., Alahi, A., Savarese, S.: Learning to track: online multi-object tracking by decision making. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4705–4713 (2015)

    Google Scholar 

18. Yu, H., et al.: Groupwise tracking of crowded similar-appearance targets from low-continuity image sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 952–960 (2016)

    Google Scholar 

19. Lin, C.C., Hung, Y.: A prior-less method for multi-face tracking in unconstrained videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 538–547 (2018)

    Google Scholar 

20. Tijtgat, N., Van Ranst, W., Goedeme, T., Volckaert, B., De Turck, F.: Embedded real-time object detection for a UAV warning system. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2110–2118 (2017)

    Google Scholar 

21. Mueller, M., Smith, N., Ghanem, B.: A benchmark and simulator for UAV tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 445–461. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_27

    Chapter  Google Scholar 

22. Jaramillo-Avila, U., Anderson, S.R.: Foveated image processing for faster object detection and recognition in embedded systems using deep convolutional neural networks. In: Martinez-Hernandez, U., et al. (eds.) Living Machines 2019. LNCS (LNAI), vol. 11556, pp. 193–204. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24741-6_17

    Chapter  Google Scholar 

23. Cojocea, E., Hornea, S., Rebedea, T.: Balancing between centralized vs. edge processing in IoT platforms with applicability in advanced people flow analysis. In: 2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet), pp. 1–6. IEEE (2019)

    Google Scholar 

24. Raspberry Pi 4. https://www.raspberrypi.org/products/raspberry-pi-4-model-b/. Accessed 31 Jan 2020

25. Intel Neural Compute Stick 2. https://software.intel.com/en-us/neural-compute-stick. Accessed 31 Jan 2020

26. Multiple Object Tracking Benchmark MOT17. https://motchallenge.net/data/MOT17/. Accessed 31 Jan 2020

27. Bernardin, K., Stiefelhagen, R.: Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP J. Image Video Process. 2008, 1–10 (2008)

    Article  Google Scholar 

28. https://magpi.raspberrypi.org/articles/raspberry-pi-4-vs-raspberry-pi-3b-plus. Accessed 30 Apr 2020

Download references