{'fraction_non_alphanumeric': 0.025280898876404494, 'fraction_numerical': 0.0018726591760299626, 'mean_word_length': 5.6397515527950315, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Automatic detection and tracking of reappearing targets in forward-looking infrared imagery

Target detection and tracking algorithms deal with the recognition of a variety of target images obtained from a multitude of sensor types, such as forward-looking infrared (FLIR), synthetic aperture radar and laser radar.1,2 Temporary disappearance and then reappearance of the target(s) in the field-of-view may be encountered during the tracking processes. To accommodate this problem, training based techniques have been developed using combination of two techniques; tuned basis functions (TBF) and correlation based template matching (TM) techniques. The TBFs are used to detect possible tentative target images. The detected candidate target images are then introduced into the second algorithm, called clutter rejection module, to determine the target reentering frame and location of the target. The performance of the proposed TBF-TM based reappeared target detection and tracking algorithm has been tested using real-world forward looking infrared video sequences.