Video Monitoring Behaviors of Captive-farmed Porcupines

YANG Wei; YU Shou-hua

doi:10.19303/j.issn.1008-0384.2017.09.018

Volume 32 Issue 9

Nov. 2017

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YANG Wei, YU Shou-hua. Video Monitoring Behaviors of Captive-farmed Porcupines[J]. Fujian Journal of Agricultural Sciences, 2017, 32(9): 1021-1025. doi: 10.19303/j.issn.1008-0384.2017.09.018

Citation:

YANG Wei, YU Shou-hua. Video Monitoring Behaviors of Captive-farmed Porcupines[J]. Fujian Journal of Agricultural Sciences, 2017, 32(9): 1021-1025. doi: 10.19303/j.issn.1008-0384.2017.09.018

YANG Wei, YU Shou-hua. Video Monitoring Behaviors of Captive-farmed Porcupines[J]. Fujian Journal of Agricultural Sciences, 2017, 32(9): 1021-1025. doi: 10.19303/j.issn.1008-0384.2017.09.018

Citation:

YANG Wei, YU Shou-hua. Video Monitoring Behaviors of Captive-farmed Porcupines[J]. Fujian Journal of Agricultural Sciences, 2017, 32(9): 1021-1025. doi: 10.19303/j.issn.1008-0384.2017.09.018

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Video Monitoring Behaviors of Captive-farmed Porcupines

doi: 10.19303/j.issn.1008-0384.2017.09.018

YANG Wei,
YU Shou-hua^,

College of Mathematic and Informatic, South China Agricultural University, Guangzhou, Guangdong 510642, China

Received Date: 2017-03-07
Rev Recd Date: 2017-06-01
Publish Date: 2017-09-28

Abstract

Abstract

To understand the living habits for remotely managing the breeding of captive-farmed porcupines, this study applied video to monitor and establish a recognition model with the aid of computation for the behaviors of the animals. Firstly, the mixed Gaussian background modeling was used to build a movement contour model of the porcupines in the pan. Using 3 chosen classifiers, the marked scenes of porcupine activities were categorized with an accuracy of 86.34%. Subsequently, ORB key points were introduced as an additional attribute for the classification which raised the accuracy to 93.23%. The resulting model could now recognize 7 basic behaviors, including resting, eating, drinking, excretion, and chewing an iron gate or a water trough, of porcupines in captivity.
- captive-farmed porcupine,
- mixed gaussian model,
- background modeling,
- ORB detection,
- support vector machines,
- data mining

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References(18)

References

[1]	陆明洲, 沈明霞, 丁永前等.畜牧信息智能监测研究进展[J].中国农业科学, 2012, 45(14):2939-2947. doi: 10.3864/j.issn.0578-1752.2012.14.017
[2]	OCZAK M, ISMAYILOVA G, COSTA A, et al. Analysis of aggressive behaviours of pigs by automatic video recordings[J]. Computers & Electronics in Agriculture, 2013, 99(C):209-217.
[3]	OTT S, MOONS C P H, KASHIHA M A, et al. Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities[J]. Livestock Science, 2014, 160(1):132-137.
[4]	朱燕. 基于时空兴趣点的猪的跛脚行为识别[D]. 镇江: 江苏大学, 2016.
[5]	劳凤丹, 滕光辉, 李军, 等.机器视觉识别单只蛋鸡行为的方法[J].农业工程学报, 2012, 28(24):157-163. http://d.wanfangdata.com.cn/Periodical/nygcxb201224024
[6]	何东健, 孟凡昌, 赵凯旋, 等.基于视频分析的犊牛基本行为识别[J].农业机械学报, 2016, 47(9):294-300. doi: 10.6041/j.issn.1000-1298.2016.09.040
[7]	洪留荣.应用轮廓变化信息的实验鼠行为识别[J].计算机工程, 2014, 40(3):213-217, 223. http://d.wanfangdata.com.cn/Periodical/jsjgc201403045
[8]	赵利强. 基于移动轨迹分析的大鼠行为识别研究[D]. 杭州: 浙江大学, 2016.
[9]	陈吉宏, 俞守华, 区晶莹.猪舍智能监控系统中猪只识别算法研究[J].广东农业科学, 2011, 38(10):151-153. doi: 10.3969/j.issn.1004-874X.2011.10.056
[10]	陈显周, 俞守华, 区晶莹.异常挖掘在猪只行为数据分析上的应用[J].农业现代化研究, 2011, 32(S1):52-55.
[11]	许丹纯, 俞守华, 区晶莹等.可拓分析法在猪场环境安全预警中的应用[J].广东农业科学, 2011, 38(23):160-163. doi: 10.3969/j.issn.1004-874X.2011.23.054
[12]	周勇钧, 俞守华, 区晶莹.多特征Camshift和Kalman滤波结合的猪只智能跟踪[J].广东农业科学, 2013, 40(9):174-177, 188. http://d.wanfangdata.com.cn/Periodical/gdnykx201309049
[13]	YU S. Tracking Algorithm Based on Multi-feature Detection and Target Association of Pigs on Large-scale Pig Farms[J]. Journal of Information & Computational Science, 2015, 12(10):3837-3844.
[14]	SHI J. Good features to track[D].New York:Cornell University, 1993.
[15]	BAY H, ESS A, TUYTELAARS T, et al. Speeded-Up Robust Features[J]. Computer Vision & Image Understanding, 2008, 110(3):404-417. http://d.wanfangdata.com.cn/Periodical/jsjyyyj201303072
[16]	LINDEBERG T. Scale Invariant Feature Transform[J]. Scholarpedia, 2012, 7(5):2012-2021. http://d.wanfangdata.com.cn/Periodical/xtgcydzjs-e201506022
[17]	RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB:An efficient alternative to SIFT or SURF[C]//IEEE International Conference on Computer Vision. IEEE, 2011:2564-2571.
[18]	陶新民, 曹盼东, 宋少宇, 等.基于半监督高斯混合模型核的支持向量机分类算法[J].信息与控制, 2013, 42(1):18-26. http://d.wanfangdata.com.cn/Periodical/xxykz201301004