Multi-object Tracking Within Air-Traffic-Control Surveillance Videos

In this paper, we strive to settle Multi-object tracking (MOT) problem within Air-Traffic-Control (ATC) surveillance videos. The uniqueness and challenges of the specific problem at hand is two-fold. Firstly, the targets within ATC surveillance videos are small and demonstrate homogeneous appearance. Secondly, the number of targets within the tracking scene undergoes severe variations results from multiple reasons. To solve such a problem, we propose a method that combines the advantages of fast association algorithm and local adjustment technique under a general energy minimization framework. Specifically, a comprehensive and discriminative energy function is established to measure the probability of hypothetical movement of targets, and the optimal output of the function yields to the most responsible target state configuration. Extensive experiments prove the effectiveness of our method on this new dataset.