{'fraction_non_alphanumeric': 0.016634050880626222, 'fraction_numerical': 0.0, 'mean_word_length': 5.515923566878981, '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}}
Robust Automatic Tracking of Skin-Colored Objects with Level Set based Occlusion Handling

The automatic detection and tracking of multiple skin-colored objects in videos plays important roles in gestural human-computer interaction, such as in gesture and sign language recognition. In this paper, we propose a hybrid tracking framework, which consistently combines the blob based temporal data association and the level set based spatial occlusion handling through online shape learning, to track variable number of skin-colored objects. Blob based data association can provide rapid and accurate tracking when there’s no occlusion; while level set based occlusion handling can recover the occluded shape as precisely as possible. In this way, the robust complete region tracking to preserve and provide much more useful information for future high-level vision tasks is guaranteed. We demonstrate the effectiveness and efficiency of this approach by experimenting on several video sequences with complex motion patterns.