LAUSR

Flying bird detection has recently attracted increasing attention in computer vision, which becomes an urgent task with the opening up of the low-altitude airspace. However, compared to conventional object detection tasks, it is much more challenging to trap flying birds in aerial videos due to small target sizes, complex backgrounds of great variations and disturbances of bird-like objects. In this paper, we propose a unified framework termed glance-and-stare detection (GSD) to trap flying birds in aerial videos. The GSD is inspired by the fact that human beings first glance at the whole image and then stare at the areas where the suspected object is most likely to appear until the confirmation is obtained. Specifically, we propose the zooming-in algorithm to generate region proposals for accurate localization of flying birds; to represent region proposal sequences of different lengths, we propose adaptive deep spatio-temporal features by leveraging the strength of 3D convolutional neural networks, based on which classification is conducted to achieve final detection. In contrast to conventional methods, the GSD enables localization and classification to be conducted jointly in an alternating iterative way, which mutually enhances each other to improve their performance. In order to validate the proposed GSD algorithm, we build flying bird data sets including images and videos, which provide new benchmarks for evaluation of flying bird detection systems. Experiments on the data sets demonstrate that the GSD can achieve high detection accuracy and largely outperform the state-of-the-art detection methods.