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http://dx.doi.org/10.9717/kmms.2021.24.7.890

Accurate Pig Detection for Video Monitoring Environment  

Ahn, Hanse (Department of Computer Convergence Software, Korea University)
Son, Seungwook (Department of Computer Convergence Software, Korea University)
Yu, Seunghyun (Department of Computer Convergence Software, Korea University)
Suh, Yooil (Info Valley Korea, Inc.)
Son, Junhyung (Department of Computer Convergence Software, Korea University)
Lee, Sejun (Department of Computer Convergence Software, Korea University)
Chung, Yongwha (Department of Computer Convergence Software, Korea University)
Park, Daihee (Department of Computer Convergence Software, Korea University)
Publication Information
Journal of Korea Multimedia Society / v.24, no.7, 2021 , pp. 890-902 More about this Journal
Abstract
Although the object detection accuracy with still images has been significantly improved with the advance of deep learning techniques, the object detection problem with video data remains as a challenging problem due to the real-time requirement and accuracy drop with occlusion. In this research, we propose a method in pig detection for video monitoring environment. First, we determine a motion, from a video data obtained from a tilted-down-view camera, based on the average size of each pig at each location with the training data, and extract key frames based on the motion information. For each key frame, we then apply YOLO, which is known to have a superior trade-off between accuracy and execution speed among many deep learning-based object detectors, in order to get pig's bounding boxes. Finally, we merge the bounding boxes between consecutive key frames in order to reduce false positive and negative cases. Based on the experiment results with a video data set obtained from a pig farm, we confirmed that the pigs could be detected with an accuracy of 97% at a processing speed of 37fps.
Keywords
Real-Time Video Monitoring; Video Object Detection; Pig Detection; Image Processing; Deep Learning; YOLO;
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1 S. Mathews, A. Miller, J. Clapp, T. Plotz, and I. Kyriazakis, "Early Detection of Health and Welfare Compromises through Automated Detection of Behavioural Changes in Pigs," The Veterinary Journal, Vol. 217, pp. 43-51, 2016.   DOI
2 H. Zhu, H. Wei, B. Li, X. Yuan, and N. Kehtarnavaz, "A Review of Video Object Detection: Datasets, Metrics, and Methods," Applied Science, Vol. 10, No. 21, pp. 7834, 2020.   DOI
3 L. Jiao, et al. "New Generation Deep Learning for Video Object Detection: A Survey," IEEE Transactions on Neural Networks and Learning Systems, pp. 1-21, 2021.
4 X. Zhu, Y. Xiong, J. Dai, L. Yuan, and Y. Wei, "Deep Feature Flow for Video Recognition," Proc. Conference on Computer Vision and Pattern Recognition, pp. 4141-4150, 2017.
5 S. Wang, Y. Zhou, J. Yan, and Z. Deng, "Fully Motion-Aware Network for Video Object Detection," Proc. European Conference on Computer Vision, pp. 557-573, 2018.
6 J. Seo, H. Ahn, D. Kim, S. Lee, Y. Chung, and D. Park, "EmbeddedPigDet: Fast and Accurate Pig Detection for Embedded Board Implementations," Applied Sciences, Vol. 10, No. 8, pp. 2878, 2020.   DOI
7 C. Feichtenhofer, A. Pinz, and A. Zisserman, "Detect to Track and Track to Detect," Proc. Conference on Computer Vision and Pattern Recognition, pp. 3038-3046, 2017.
8 A. Bochkovskiy, C. Wang, and H. Liao, "Yolov 4: Optimal Speed and Accuracy of Object Detection," arXiv Preprint, arXiv:2004.10934, 2020.
9 Y. Chai, "Patchwork: A Patch-Wise Attention Network for Efficient Object Detection and Segmentation in Video Streams," Proc. International Conference on Computer Vision, pp. 3415-3424, 2019.
10 H. Belhassen, H. Zheng, V. Fresse, and E. Bourennane, "Improved Video Object Detection by Seq-Bbox Matching," Proc. International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application, pp. 226-233, 2019.
11 Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye, and D. Ren, "Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression," Proc. Conference on Artificial Intelligence, 2020.
12 Open Source Computer Vision: 'OpenCV'(2021). https://opencv.org (accessed April 2021).
13 K. Zuiderveld, Contrast Limited Adaptive Histogram Equalization, Academic Press Inc, Cambridge, MA, USA, 1994.
14 J. Sa, Y. Choi, H. Lee, Y. Chung, D. Park, and J. Cho, "Fast Pig Detection with a Top-View Camera Under Various Illumination Conditions," Symmetry, Vol. 11, No. 2, pp. 266, 2019.   DOI
15 J. Kim, et al. "Depth-Based Detection of Standing-Pigs in Moving Noise Environments," Sensors, Vol. 17, No. 12, pp. 2757, 2017.   DOI
16 N. Wojke, A. Bewley, and D. Paulus, "Simple Online and Realtime Tracking with a Deep Association Metric," Proc. International Conference on Image Processing, pp. 3645-3649, 2017.
17 F. Xiao and Y. Lee, "CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video," Proc. European Conference on Computer Vision, pp 485-501, 2018.
18 W. Han, et al. "Seq-NMS for Video Object Detection," Proc. Conference on Computer Vision and Pattern Recognition, arXiv:1602.08465, 2016.
19 J. Xu, M. Yang, and Y. Wu, "Multi-Model Ensemble with Rich Spatial Information for Object Detection," Pattern Recognition, Vol. 99, pp. 107098, 2020.   DOI
20 M. Ju, et al. "A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring," Sensors, Vol. 18, No. 6, pp. 1746, 2018.   DOI
21 Y. Chung, H. Kim, H. Lee, D. Park, T. Jeon, and H. Chang, "A Cost-Effective Pigsty Monitoring System Based on a Video Sensor," KSII Transaction on Internet and Information Systems, Vol. 8, No. 4, 2014.
22 K. Kang, et al. "Object Detection in Videos with Tubelet Proposal Networks," Proc. Conference on Computer Vision and Pattern Recognition, pp. 889-897, 2017.
23 F. Xiao and Y. Lee, "Video Object Detection with an Aligned Spatial-Temporal Memory," Proc. European Conference on Computer Vision, pp. 485-501, 2018.
24 G. Bertasius, L. Torresani, and J. Shi, "Object Detection in Video with Spatiotemporal Sampling Networks," Proc. European Conference on Computer Vision, pp. 331-346, 2018.
25 J. Seo, M. Ju, Y. Choi, J. Lee, Y. Chung, and D. Park, "Separation of Touching Pigs using YOLO-Based Bounding Box," Journal of Korea Multimedia Society, Vol. 21, No. 2, pp. 77-86, 2018   DOI
26 S. Lee, H. Ahn, J. Seo, Y. Chung, D. Park, and S. Pan, "Practical Monitoring of Undergrown Pigs for IoT-Based Large-Scale Smart Farm," IEEE Access, Vol. 7, pp. 173796-173810, 2019.   DOI
27 Korea Rural Economic Institute(2019). https://www.krei.re.kr (accessed July 2021).
28 K. Jun, S. Kim, and H. Ji, "Estimating Pig Weights from Images Without Constraint on Posture and Illumination," Computers and Electronics in Agriculture, Vol. 153, pp. 169-176, 2018.   DOI