Individual Identification Method for Group-Housed Pigs Based on Optimal Feature Extraction

Abstract:

In order to reduce the workload of farmers on individual identification of pigs, reduce interference to pigs, and raise automation and intelligent level of farms, a Matlab-based automatic classification system is set up. Firstly, top view video sequences are captured for group-housed pigs from pig barn, and the adaptive Gaussian mixture model based on prediction mechanism is used to detect the foreground objects. Then with morphological processing and hole filling, the targets are extracted as completely as possible from the background. Secondly, after building the sample image databases for each pig, color, texture and shape features are extracted and combined as the feature vector. Next, LLE (Locally Linear Embedding) algorithm is adopted to reduce the data dimension. Finally, hybrid kernel SVM (Support Vector Machine) classifier is designed to identify the pigs in test frames. Experimental results show that the recognition rate of pigs is 95.2%. For improving the recognition rate, decreasing cost and interference to pigs, the above method is proposed. Moreover, it can lay a foundation for future researches on group-housed pigs in behaviors, emotions, breathings and so on, which has good practical significance.

• Periodical: Applied Mechanics and Materials (Volume 614)
• Pages: 436-439
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.614.436
• Online since: September 2014
• Authors: Wei Xing Zhu*, Jia Li Chen, Yi Zheng Guo
• Keywords: Data Optimization, Feature Extraction, Group-Housed Pigs, Multi-Target Recognition, Support Vector Machine (SVM)

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