{'fraction_non_alphanumeric': 0.023956723338485315, 'fraction_numerical': 0.0, 'mean_word_length': 5.410891089108911, '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}}
Recognizing wide-area and process-type activities

New methods are presented to model, visualize and automatically recognize wide-area activities, which essentially are activities that span large areas (such as a facility or urban neighborhood) and that usually span long time intervals (such as hours and weeks). We introduce the no-go topology method and the chokepoint-observation interaction method, and then show how new algorithms can be built on them to recognize a category of wide-area activity, called process-type activities. Experimental results are presented for recognizing a manufacturing process observed via persistent GMTI sensor data. Then we present experimental results illustrating how an interesting activity can be detected as a deviation from a learned wide-area normalcy model, and how new wide- area activity patterns can be discovered using simple visualizations of the results. One objective of this paper is to demonstrate that it is theoretically possible to recognize wide-area and process-type activities in built-up environments using GMTI data. The results presented here use somewhat ideal sensor data (small positional error ellipses, continuous GMTI observations, repetitive activities) and our approach is to move toward more operationally realistic data.