Flying Objects Classification Using Trajectory Characterization

This paper introduces a method for classifying and recognizing flying objects using trajectory features and artificial neural networks (ANN). Initially, the video sequence undergoes processing through a Gaussian mixture model (GMM) to detect and track the flying objects. Then, we will extract trajectory features from our dataset and use them to feed ANN for flying object classification. These features include turning angle, speed, acceleration and centroid distance function. The classical ANN is applied for feature vector classification to discriminate between birds and drones. Experimental results are conducted to showcase the effectiveness of the proposed method in classifying drones and birds. Moreover, the automated approach can be valuable in aiding military services to differentiate drones from other objects.