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Through the establishment of video sensor networks, allowing re- searchers to do long-term field observations better in their offices. But the study of morphology classification and behavior identification of animals from mass video data is very costly and time-consuming repetitive work. For this, we did video pre- processing and multiple targets recognition and tracking. We built a classification model by support vector machine algorithm. Used 384 morphological records of bar-headed geese from the scientific video obtained in Qinghai Lake, we extracted a vector constructed 9 features in-cluing aspect ratio, shape factor and 7 Hu inva- riant moments respectively from each record and thus got 384 vectors in total, in which 289 were used for training and 95 for testing. The morphology classifica- tion accuracy reached 65.3%. And we used bar-headed geese movement parame- ters and bar-headed geese posture information; we analyzed and identified bar- headed geese four basic behaviors, and achieve a certain recognition rate. This will help the researches to do study the normal behavior and abnormal behavior of the bar-headed geese and the reserve personnel to do daily monitoring of the bar- headed geese.

Morphology classification and behaviors identification of birds in scientific video