Advertisement

Retail Traffic-Flow Analysis Using a Fast Multi-object Detection and Tracking System

  • Richard Cobos
  • Jefferson Hernandez
  • Andres G. AbadEmail author
Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 1096)

Abstract

Traffic-flow analysis allows to make critical decisions for retail operation management. Common approaches for traffic-flow analysis make use of hardware-based solutions, which have major drawbacks, such as high deployment and maintenance costs. In this work, we address this issue by proposing a Multiple-Object Tracking (MOT) system, following the tracking-by-detection paradigm, that leverages on an ensemble of detectors, each running every f frames. We further measured the performance of our model in the MOT16 Challenge and applied our algorithm to obtain heatmaps and paths for customers and shopping carts in a retail store from CCTV cameras.

Keywords

Multi-object tracking Shopping-carts detection Indoor localization and tracking Kalman filters 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgment

The authors would like to acknowledge the stimulating discussions and help from Victor Merchan, Joo Wang Kim and Ricardo Palacios, as well as Tiendas Industriales Asociadas Sociedad Anonima (TIA S.A.), a leading grocery retailer in Ecuador, for providing funding for this research effort.

References

  1. 1.
    Baisa, N.L., Wallace, A.: Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking. J. Vis. Commun. Image Represent. 59, 257–271 (2019)CrossRefGoogle Scholar
  2. 2.
    Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1806–1819 (2011)CrossRefGoogle Scholar
  3. 3.
    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. IEEE (2016)Google Scholar
  4. 4.
    Bodla, N., Singh, B., Chellappa, R., Davis, L.S.: Soft-NMS–improving object detection with one line of code. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 5562–5570. IEEE (2017)Google Scholar
  5. 5.
    Cheng, Q.J., Ng, J.K.Y., Shum, K.C.Y.: A wireless LAN location estimation system using center of gravity as an algorithm selector for enhancing location estimation. In: 2012 IEEE 26th International Conference on Advanced Information Networking and Applications, pp. 261–268. IEEE (2012)Google Scholar
  6. 6.
    Cobos, R., Hernandez, J., Abad, A.G.: A fast multi-object tracking system using an object detector ensemble. In: IEEE 2nd Colombian Conference on Applications in Computational Intelligence (ColCACI) (2019)Google Scholar
  7. 7.
    Contigiani, M., Pietrini, R., Mancini, A., Zingaretti, P.: Implementation of a tracking system based on UWB technology in a retail environment. In: 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp. 1–6. IEEE (2016)Google Scholar
  8. 8.
    Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge 2007 (voc2007) results (2007)Google Scholar
  9. 9.
    Fang, K., Xiang, Y., Li, X., Savarese, S.: Recurrent autoregressive networks for online multi-object tracking. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 466–475. IEEE (2018)Google Scholar
  10. 10.
    Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181–207 (2003)CrossRefGoogle Scholar
  11. 11.
    Kurkova, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.): Artificial Neural Networks and Machine Learning ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, 4–7 October 2018, Proceedings, Part I. Theoretical Computer Science and General Issues. Springer International Publishing (2018)Google Scholar
  12. 12.
    Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: Light-head R-CNN: In defense of two-stage object detector. arXiv preprint arXiv:1711.07264 (2017)
  13. 13.
    Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10602-1_48CrossRefGoogle Scholar
  14. 14.
    Mahmoudi, N., Ahadi, S.M., Rahmati, M.: Multi-target tracking using CNN-based features: Cnnmtt. Multimedia Tools and Applications, pp. 1–20 (2018)CrossRefGoogle Scholar
  15. 15.
    Milan, A., Leal-Taixé, L., Reid, I., Roth, S., Schindler, K.: Mot16: A benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831 (2016)
  16. 16.
    Paszke, A., et al.: Automatic differentiation in pytorch. In: NIPS-W (2017)Google Scholar
  17. 17.
    Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1201–1208. IEEE (2011)Google Scholar
  18. 18.
    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
  19. 19.
    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
  20. 20.
    Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 17–35. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-48881-3_2CrossRefGoogle Scholar
  21. 21.
    Sanchez-Matilla, R., Poiesi, F., Cavallaro, A.: Online multi-target tracking with strong and weak detections. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 84–99. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-48881-3_7CrossRefGoogle Scholar
  22. 22.
    Stillman, S., Tanawongsuwan, R., Essa, I.: Tracking multiple people with multiple cameras, January 1999Google Scholar
  23. 23.
    Tang, S., Andres, B., Andriluka, M., Schiele, B.: Subgraph decomposition for multi-target tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5033–5041 (2015)Google Scholar
  24. 24.
    Tang, S., Andres, B., Andriluka, M., Schiele, B.: Multi-person tracking by multicut and deep matching. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 100–111. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-48881-3_8CrossRefGoogle Scholar
  25. 25.
    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. IEEE (2017)Google Scholar
  26. 26.
    Yu, F., Li, W., Li, Q., Liu, Y., Shi, X., Yan, J.: POI: multiple object tracking with high performance detection and appearance feature. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 36–42. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-48881-3_3CrossRefGoogle Scholar
  27. 27.
    Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
  28. 28.
    Zhou, Z., Xing, J., Zhang, M., Hu, W.: Online multi-target tracking with tensor-based high-order graph matching. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 1809–1814. IEEE (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Industrial Artificial Intelligence (INARI) Research LabEscuela Superior Politecnica del Litoral (ESPOL)GuayaquilEcuador

Personalised recommendations

Cite paper

Buy options