Gesture Recognition of Pigs Based on Wavelet Moment and Probabilistic Neural Network

Abstract

For real-time monitoring the behavior of pigs in piggery, the method that combined the advantages of wavelet multi-scale analysis with invariant moments is proposed. Firstly, the original image is pre-processed by using ant colony algorithm to extract object contour. Then the target contour edge growth method and binary morphology are used, and the outlines of pigs are extracted by canny operator. Wavelet moment was used to get the global features of an image and increase the structural details of the image feature description. Finally, the neural network is applied to identify four behaviors including normal walking, walking down, looked up walking and lying of pigs. Experimental results show that the accuracy of the classification and identification of swine gesture reached more than 95%. This method has a better effect in the recognition of pigs and the noise resistance.