Abstract
Automatic pig counting with pattern recognition and computer vision techniques, despite its significance in intelligent agriculture, remains to be a relatively unexplored area and calls for further study. In this paper, we propose a large-scale image-based Pig Counting in Real world (PCR) dataset, covering a variety of real-world scenarios and environmental factors. The dataset consists of two subsets, i.e., PartA captured on real-world pig pens and PartB collected from the Internet, with center point annotations of pig torsos in 4844 images. Moreover, we develop an automatic pig counting algorithm based on weakly-supervised instance segmentation, which can output a single segmentation blob per instance via the proposed Segmentation-Split-Regression (SSR) loss, utilizing point-level annotations only. Experiments show that the proposed algorithm achieves state-of-the-art counting accuracy and exhibits superior robustness against challenging environmental factors. The dataset and source codes are available at https://github.com/jierujia0506/PCR.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akiva, P., Dana, K.J., Oudemans, P.V., Mars, M.: Finding berries: segmentation and counting of cranberries using point supervision and shape priors. In: CVPR Workshops, pp. 219–228 (2020)
Arteta, C., Lempitsky, V., Zisserman, A.: Counting in the wild. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 483–498. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_30
Chattopadhyay, P., Vedantam, R., Selvaraju, R.R., Batra, D., Parikh, D.: Counting everyday objects in everyday scenes. In: CVPR, pp. 4428–4437 (2017)
Chen, G., Shen, S., Wen, L., Luo, S., Bo, L.: Efficient pig counting in crowds with keypoints tracking and spatial-aware temporal response filtering. In: ICRA, pp. 10052–10058 (2020)
Chowdhury, P.N., Shivakumara, P., Nandanwar, L., Samiron, F., Pal, U., Lu, T.: Oil palm tree counting in drone images. Pattern Recognit. Lett. 153, 1–9 (2022)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2015)
Laradji, I.H., Rostamzadeh, N., Pinheiro, P.O., Vazquez, D., Schmidt, M.: Where are the blobs: counting by localization with point supervision. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 560–576. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_34
Li, Y., Zhang, X., Chen, D.: CSRNet: dilated convolutional neural networks for understanding the highly congested scenes. In: CVPR, pp. 1091–1100 (2018)
Lin, T.Y., Goyal, P., Girshick, R.B., He, K., Dollár, P.: Focal loss for dense object detection. IEEE Trans. Pattern Ana. Mach. Intell 42, 318–327 (2020)
Lin, W., Chan, A.B.: Optimal transport minimization: crowd localization on density maps for semi-supervised counting. In: CVPR, pp. 21663–21673 (2023)
Liu, C., Su, J., Wang, L., Lu, S., Li, L.: LA-DeepLab V3+: a novel counting network for pigs. Agriculture 12(2), 284 (2022)
Naudé, J.J., Joubert, D.: The aerial elephant dataset: a new public benchmark for aerial object detection. In: CVPR Workshops (2019)
Rao, J., Xu, T., Song, X., Feng, Z., Wu, X.: KITPose: keypoint-interactive transformer for animal pose estimation. In: Yu, S., et al. (eds.) Pattern Recognition and Computer Vision. PRCV 2022. LNCS, vol. 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_51
Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR, pp. 779–788 (2016)
Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Ana. Mach. Intell 39, 1137–1149 (2015)
Rey, N., Volpi, M., Joost, S., Tuia, D.: Detecting animals in African savanna with UAVs and the crowds. Remote Sens. Environ. 200, 341–351 (2017)
Shao, W., Kawakami, R., Yoshihashi, R., You, S., Kawase, H., Naemura, T.: Cattle detection and counting in UAV images based on convolutional neural networks. Int. J. Remote Sens. 41, 31–52 (2020)
Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: CVPR, pp. 5686–5696 (2019)
Tian, M., Guo, H., Chen, H., Wang, Q., Long, C., Ma, Y.: Automated pig counting using deep learning. Comput. Electron. Agric. 163, 104840 (2019)
Vincent, L.M., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Ana. Mach. Intell 13, 583–598 (1991)
Wang, Q., Gao, J., Lin, W., Yuan, Y.: Learning from synthetic data for crowd counting in the wild. In: CVPR, pp. 8190–8199 (2019)
Wu, F., et al.: Detection and counting of banana bunches by integrating deep learning and classic image-processing algorithms. Comput. Electron. Agric. 209, 107827 (2023)
Wu, K., Otoo, E.J., Shoshani, A.: Optimizing connected component labeling algorithms. In: SPIE Medical Imaging (2005)
Wu, Z., Fuller, N.W., Theriault, D.H., Betke, M.: A thermal infrared video benchmark for visual analysis. In: CVPR Workshops, pp. 201–208 (2014)
Zand, M., Damirchi, H., Farley, A., Molahasani, M., Greenspan, M., Etemad, A.: Multiscale crowd counting and localization by multitask point supervision. In: ICASSP (2022)
Zhang, Y., Zhou, D., Chen, S., Gao, S., Ma, Y.: Single-image crowd counting via multi-column convolutional neural network. In: CVPR, pp. 589–597 (2016)
Zhou, B., Wang, S., Xiao, S.: Double recursive sparse self-attention based crowd counting in the cluttered background. In: PRCV (2022)
Zhu, P., Peng, T., Du, D., Yu, H., Zhang, L., Hu, Q.: Graph regularized flow attention network for video animal counting from drones. IEEE Trans. Image Process. 30, 5339–5351 (2021)
Acknowledgment
This work was supported by the National Natural Science Foundation of China (62106133).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jia, J., Zhang, S., Ruan, Q. (2024). PCR: A Large-Scale Benchmark for Pig Counting in Real World. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14428. Springer, Singapore. https://doi.org/10.1007/978-981-99-8462-6_19
Download citation
DOI: https://doi.org/10.1007/978-981-99-8462-6_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8461-9
Online ISBN: 978-981-99-8462-6
eBook Packages: Computer ScienceComputer Science (R0)