{'fraction_non_alphanumeric': 0.011627906976744186, 'fraction_numerical': 0.0, 'mean_word_length': 4.920634920634921, '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}}
Fast good features selection for wide area monitoring

Recently the surveillance of wide areas has pointed the interest of the research community. The use of active vision seems to be the most effective solutions for these needs. Against the better acquiring resolution there is the problem of the apparent motion inducted by the camera motion known as ego-motion. Feature based methods for ego-motion estimation are widely used in computer vision but they deal with feature recovery and with errors in feature tracking. In this paper, we propose a fast method to extract and select new features during camera motion. This is achieved by adopting a reference map containing well trackable features that is updated at each frame by introducing new good features related to regions appearing in the current image. A new procedure is applied to reject badly tracked features. The current frame and the background after compensation are processed by a change detection method in order to locate mobile objects. Results are presented in the context of a visual-based surveillance system for monitoring outdoor environments.