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.

Key words: captive-farmed porcupine, mixed gaussian model, background modeling, ORB detection, support vector machines, data mining