This book presents a novel technique for simultaneous multiple target detection and tracking. In doing so, an algorithm has been formulated, which is based on class-associative target detection strategy that recognizes and tracks multiple dissimilar or class-associative targets in gray-scale image sequences. Ego-motion effects are compensated through a robust correlation coefficient image registration process. Fringe-adjusted joint transform correlation combined with the proposed hetero-associative filter is used to enhance the correlation performance while ensuring equal cross-correlation peak criterion for each element of the selected class. Tracking is accomplished by combining the analysis of single image frame and motion estimation between consecutive image frames. The desired targets are identified prior to tracking by correlating successive frames using an enhanced version of the fringe-adjusted filter. The effectiveness of the detection and tracking algorithm is tested on challenging real life, forward looking infrared (FLIR) image sequences.