{'fraction_non_alphanumeric': 0.01383399209486166, 'fraction_numerical': 0.0, 'mean_word_length': 5.176829268292683, '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}}
Detecting New Stable Objects In Surveillance Video

We describe a novel method to detect new stable objects in video. This includes detecting new objects that appear in a scene and remain stationary for a period of time. Examples include detecting a dropped bag or a parked car. Our method utilizes the state transition history (or a record of the "life cycle") of individual Gaussian distributions in a Gaussian Mixture Model (GMM) used to model the background. In typical implementations of the GMM, this state transition information is ignored however we show that by observing and retaining the history of state transitions of individual distributions, it is possible to detect long term changes in a scene. In particular we identify changes to the most probable background distribution and impose certain conditions on the characteristics and temporal behavior of this distribution. Results presented in this paper illustrate the success of the proposed method and its relevance to surveillance applications