Abstract
Fruit counting and tracking is a crucial component of fruit harvesting and yield forecasting applications within horticulture. We present a novel multi-object, multi-class fruit tracking system to count fruit from image sequences. We first train a recurrent neural network (RNN) comprised of a feature extractor stem and two heads for re-identification and maturity classification. We apply the network to detected fruits in image sequences and utilise the output of both network heads to maintain track consistency and reduce intra-class false positives between maturity stages. The counting-by-tracking system is evaluated by comparing with a popular detect-to-track architecture and against manually labelled tracks (counts). Our proposed system achieves a mean average percentage error (MAPE) of 3% (L1 loss = 7) improving on the baseline multi-object tracking approach which obtained an MAPE of 21% (L1 loss = 41). Validating this approach for use in horticulture.
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References
Bellocchio, E., Ciarfuglia, T.A., Costante, G., Valigi, P.: Weakly supervised fruit counting for yield estimation using spatial consistency. IEEE Robot. Autom. Lett. 4(3), 2348–2355 (2019). https://doi.org/10.1109/LRA.2019.2903260
Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. CoRR abs/1602.00763 (2016). http://arxiv.org/abs/1602.00763
Bochinski, E., Eiselein, V., Sikora, T.: High-speed tracking-by-detection without using image information. In: International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017, Lecce, Italy, August 2017. http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf
Chen, S.W., et al.: Counting apples and oranges with deep learning: a data-driven approach. IEEE Robot. Autom. Lett. 2(2), 781–788 (2017). https://doi.org/10.1109/LRA.2017.2651944
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR abs/1311.2524 (2013). http://arxiv.org/abs/1311.2524
Grimstad, L., From, P.J.: The Thorvald II agricultural robotic system. Robotics 6(4) (2017). https://doi.org/10.3390/robotics6040024. https://www.mdpi.com/2218-6581/6/4/24
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015). http://arxiv.org/abs/1512.03385
Kirk, R., Cielniak, G., Mangan, M.: L*a*b*fruits: a rapid and robust outdoor fruit detection system combining bio-inspired features with one-stage deep learning networks. Sensors 20(1) (2020). https://doi.org/10.3390/s20010275. https://www.mdpi.com/1424-8220/20/1/275
Leal-Taixé, L., Milan, A., Schindler, K., Cremers, D., Reid, I.D., Roth, S.: Tracking the trackers: an analysis of the state of the art in multiple object tracking. CoRR abs/1704.02781 (2017). http://arxiv.org/abs/1704.02781
Lin, T., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. CoRR abs/1708.02002 (2017). http://arxiv.org/abs/1708.02002
Liu, X., et al.: Monocular camera based fruit counting and mapping with semantic data association. IEEE Robot. Autom. Lett. 4(3), 2296–2303 (2019). https://doi.org/10.1109/LRA.2019.2901987
Liu, X., et al.: Robust fruit counting: combining deep learning, tracking, and structure from motion. CoRR abs/1804.00307 (2018). http://arxiv.org/abs/1804.00307
Mekhalfi, M.L., et al.: Vision system for automatic on-tree kiwifruit counting and yield estimation. Sensors 20(15) (2020). https://doi.org/10.3390/s20154214. https://www.mdpi.com/1424-8220/20/15/4214
Milan, A., Leal-Taixé, L., Reid, I., Roth, S., Schindler, K.: MOT16: a benchmark for multi-object tracking. arXiv:1603.00831 [cs], March 2016
Ning, G., Zhang, Z., Huang, C., He, Z., Ren, X., Wang, H.: Spatially supervised recurrent convolutional neural networks for visual object tracking. CoRR abs/1607.05781 (2016). http://arxiv.org/abs/1607.05781
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. CoRR abs/1804.02767 (2018). http://arxiv.org/abs/1804.02767
Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. CoRR abs/1506.01497 (2015). http://arxiv.org/abs/1506.01497
Santos, T.T., de Souza, L.L., dos Santos, A.A., Avila, S.: Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association. Comput. Electron. Agric. 170, 105247 (2020). https://doi.org/10.1016/j.compag.2020.105247. https://www.sciencedirect.com/science/article/pii/S0168169919315765
Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. CoRR abs/1703.07402 (2017). http://arxiv.org/abs/1703.07402
Acknowledgement
This work was partially funded by the RASberry project at the University of Lincoln in affiliation with the Collaborative Training Partnershipfor Fruit Crop Research.
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Kirk, R., Mangan, M., Cielniak, G. (2021). Robust Counting of Soft Fruit Through Occlusions with Re-identification. In: Vincze, M., Patten, T., Christensen, H.I., Nalpantidis, L., Liu, M. (eds) Computer Vision Systems. ICVS 2021. Lecture Notes in Computer Science(), vol 12899. Springer, Cham. https://doi.org/10.1007/978-3-030-87156-7_17
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