Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators

TLDR: Experimental results, obtained with real FLIR image sequences, illustrating a wide variety of target and clutter variability, demonstrate the effectiveness and robustness of the proposed method.

Abstract: We propose a method for automatic target detection and tracking in forward-looking infrared (FLIR) image sequences. We use morphological connected operators to extract and track targets of interest and remove undesirable clutter. The design of these operators is based on general size, connectivity, and motion criteria, using spatial intraframe and temporal interframe information. In a first step, an image sequence is filtered on a frame-by-frame basis to remove background and residual clutter and to enhance the presence of targets. Detections extracted from the first step are passed to a second step for motion-based analysis. This step exploits the spatiotemporal correlation of the data, stated in terms of a connectivity criterion along the time dimension. The proposed method is suitable for piplined implementation or time progressive coding/transmission, since only a few frames are considered at a time. Experimental results, obtained with real FLIR image sequences, illustrating a wide variety of target and clutter variability, demonstrate the effectiveness and robustness of the proposed method.