Abstract
A robust and effective small dim object detection algorithm is the key to the success of an infrared tracking system. To help solve practical tracking problems, a detecting algorithm based on local contrast method (LCM) and Lucas–Kanade method (LK) is put forward. Firstly, the local contrast map of the input image is obtained using the local contrast measure which measures the dissimilarity between the current location and its neighborhoods. In this way, target signal enhancement and background clutter suppression are achieved simultaneously. Secondly, an adaptive threshold is applied to extract the suspected object regions. Finally, the central points of obtained regions are used as characteristic points, then LK optical flow algorithm to calculate optical flow at these points, and through the instantaneous velocity calculation and selection targets are detected. The experimental result shows that this method works perfectly and can effectively detect infrared targets under complex backgrounds.
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