Infrared Moving Small Target Detection Based on Consistency of Sparse Trajectory

Infrared search and track (IRST) systems require reliable detection of small targets in complex backgrounds. Outlier based methods are prone to high false positive rates due to the resemblance of point-like background features to small targets. The difference image-based method is an effective approach for suppressing point-like background interference; however, it has limitations in detecting slow-moving targets. In this letter, a novel sparse trajectory is proposed for moving target detection in IR videos. With a trajectory growing strategy, two kinds of trajectories from difference images, namely short sparse trajectories and long sparse trajectories, are correlated to avoid the slow-moving targets being dismissed. The strategy matches the trajectories based on the sparse trajectory intensity composed of similarity measures and optical flow consistency. Finally, real targets are extracted from candidate trajectories using trajectory filtering. Experimental results show that, in the scene with point-like background features, our method achieves the best detection rate and lowest false alarm compared to state-of-the-art methods.