Quadratic correlation filter based target tracking in FLIR image sequences

Abstract

Target tracking in forward looking infrared (FLIR) video sequences is challenging problem due to various limitations such as low signal-to-noise ratio, image blurring, partial occlusion, and low texture information, which often leads to missing targets or tracking non-target objects. To alleviate these problems, we propose the application of quadratic correlation filters using subframe approach in FLIR. The proposed filtering technique avoids the disadvantages of pixel-based image preprocessing techniques. The filter coefficients are obtained for desired target class from the training images. For real time applications, the input scene is first segmented to the subframes according to target location information from the previous frame. The subframe of interest is then correlated with correlation filters associated with target class. The obtained correlation output contains higher value that indicates the target location in the region of interest. The simulation results for target tracking in real life FLIR imagery have been reported to verify the effectiveness of the proposed technique.