{'fraction_non_alphanumeric': 0.018849206349206348, 'fraction_numerical': 0.0, 'mean_word_length': 5.5519480519480515, '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 background modeling for cluttered scenes

This paper proposes optimal background modeling scheme for the cluttered scenes. The background initialization is the first step in the process of segmenting out moving information. Concrete background model ensures the proper segmentation of moving information from the scene. Each pixel is modeled as mixture of Gaussian. Using decision criteria, background/foreground pixels are differentiated. During the background maintenance step, three different learning rates were used to update model separately. They constitute short, medium, and long term background model. Background models obtained with each learning rate are augmented using temporal median filtering separately. Finally, three background models are compared with ground truth using loss function based on the Mean Square Error (MSE). The background model with minimum MSE value is selected as optimal background model. The proposed method is tested on the standard datasets available online.