{'fraction_non_alphanumeric': 0.022894521668029435, 'fraction_numerical': 0.004088307440719542, 'mean_word_length': 5.616216216216216, '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}}
Selective Covariance-based Human Localization, Classification and Tracking in Video Streams from Multiple Cameras

In this paper a novel selective covariance-based method for human localization, classification and tracking in video streams from multiple cameras is proposed. Such methods are crucial for security and surveillance systems, smart environments and robots. The method is called selective covariance-based because before classifying the object using covariance descriptors (in this case the classes are the different people being tracked) we extract (selection) specific regions, which are definitive for the class of objects we deal with (people). In our case, the region being extracted is the human head and shoulders. In the paper new feature functions for covariance region descriptors are developed and compared to basic feature functions, and a mask, filtering out the most of the background information from region of interest, is proposed and evaluated. The use of the proposed feature functions and mask significantly improved the human classification performance (from 75% when using basic feature functions to 94.6% accuracy with the proposed method) while keeping computational complexity moderate.