Robust Infrared Maritime Target Detection via Anti-Jitter Spatial–Temporal Trajectory Consistency

When detecting diverse infrared (IR) small maritime targets on complicated scenes, the existing methods get into trouble and unsatisfactory performance. The main reasons are as follows: 1) affected by target characteristics and ambient temperature and so on, both bright and dark targets may exist on IR maritime images in practical application and 2) spatial information and temporal correlation of targets are not fully excavated. To these problems, we propose a robust anti-jitter spatial–temporal trajectory consistency (ASTTC) method, the main idea of which is to improve detection accuracy using single-frame detection followed by multi-frame decision. First, we innovatively design adaptive local gradient variation (ALGV) descriptor, which combines local dissimilarity measure with gradient magnitude distribution to enhance the local contrast for both bright and dark targets so that the suspected targets can be robustly extracted. For multi-frame decision, interframe displacement correction is necessary to eliminate the interference of IR imager motion and vibration for target codeword. We use pyramid optical flow to track feature point extracted by Shi-Tomasi to capture interframe registration coefficients. Then, the target position is corrected in the spatial–temporal domain. Finally, a robust spatial–temporal trajectory descriptor (STTD), which achieves target encoding and target trajectory consistency measurement, is designed to further confirm real targets and eliminate false targets. Experiments conducted on various real IR maritime image sequences demonstrate the applicability and robustness of ASTTC, which performs more satisfactorily than the existing methods in detection accuracy.