Multimode fusion algorithm for robust dim point-like target detection

This paper presents a multi-mode fusion algorithm for detection and tracking of dim, point-like target. The key contribution of this paper includes the effective fusion approach to harvest the advantages and complement the disadvantages of various algorithms using conditional voting. From qualitative analysis, these algorithms are separated into two classes, i.e. main and supporting algorithms. In the multi-mode fusion algorithm high confidence is placed on the main algorithms with supporting algorithms used to further reduce the false alarm. The main algorithms trigger a voting process and detection is confirmed true if any of the supporting algorithms report detection. The multi-modal fusion algorithm has lower false alarm and moderate true detection rate compared to any individual algorithm namely, Triple Temporal Filter, Frame Differencing, Continuous Wavelet Transform, Max-median and 2-D Mexican hat filter. Besides, a novel variability filter is proposed to remove strong glint thus reduces false alarm. Kalman filter is used to track the detected targets. A novel track decision algorithm to continue or terminate the track when target disappears is proposed. Prior knowledge of target in Kalman filter is fed forward to an Adaptive 3-D Matched filter to improve the performance. Three sets of real-world infrared image sequences with very different background and target characteristics were used to test the robustness of the multi-modal fusion algorithm. The algorithm performs satisfactorily in all the image sequences. Video clips will also be presented.