{'fraction_non_alphanumeric': 0.013280212483399735, 'fraction_numerical': 0.0, 'mean_word_length': 5.556521739130435, '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}}
Optimal object association from pairwise evidential mass functions

Object association is often a prior step in the data fusion process, especially for multiple objects tracking and multisensor data fusion. The approach introduced in this paper associates objects detected in a scene by two sensors, while modeling uncertainty using the Dempster-Shafer theory of belief functions. Sensor information is transformed into pairwise mass functions, which are combined using Dempster's rule of combination. The result of this combination allows us to find the most plausible relation between two sets of objects by solving a linear programming problem. Experimental results with real data acquired from sensors embedded in intelligent vehicles are presented.