关键词: 无人飞行器, 协同训练, 图切割, 阴影检测

Abstract: With the rapid development of unmanned aerial vehicles, character verification and behavior recognition based on pedestrian dynamic shadow biometrics in the top-view state has become an important development direction, and obtaining pedestrian dynamic shadow biometrics from low-altitude UAV platforms has become a more challenging research hotspot. Most of the existing shadow extraction methods are based on fixed cameras, which are not suitable for motion platforms. In this paper, it combines machine learning and graph cutting theory to propose a new dynamic human shadow detection method for low-altitude UAV platforms. First, two independent views for co-training are constructed according to pixel features and regional features. It uses SVM as the classifier to semi-automatically extract shadow biometric features by machine learning methods. Then, the data items of the minimum energy equation are constructed based on the above motion results and the constraint terms of the energy equation are constructed according to the gradient features of the image. And it uses the graph cutting theory to optimize the above extraction results. The experimental results show that the proposed method has a better effect than the pure collaborative training method, and can further optimize the quality of shadow biometrics obtained under the low-altitude UAV platform.

Key words: unmanned aerial vehicle, collaborative training, graph cutting, shadow detection