{'fraction_non_alphanumeric': 0.013084112149532711, 'fraction_numerical': 0.0, 'mean_word_length': 5.570552147239264, '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}}
Person tracking in camera networks using graph-based bayesian inference

In this paper, a probabilistic approach for tracking multiple persons through a network of distributed cameras is presented. The approach deals with the main problems associated with the tracking of persons through wide area networks - bridging large observation gaps between camera views and reidentifying persons - by building on robust and view-invariant high-level features as well as a highly error-tolerant probabilistic filtering of person locations. The extraction quality and discriminative power of the proposed features is evaluated on realistic data including well-known and established benchmark datasets. A comparative performance analysis is then made to assess the accuracy of the probabilistic inter-camera tracking method given a number of different simulated and real quality levels of the underlying person detection and feature extraction components. The experiments are made using a simulated virtual environment involving multiple persons in an indoor surveillance scenario.