{'fraction_non_alphanumeric': 0.014580801944106925, 'fraction_numerical': 0.0, 'mean_word_length': 5.103703703703704, '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}}
Scene-Specific Pedestrian Detector Using Monte Carlo Framework and Faster R-CNN Deep Model: PhD Forum

In this work, we propose a novel approach to automatically specialize a generic pedestrian detector to specific scene by utilizing the sequential Monte Carlo filter and the Faster R-CNN deep model. The main idea is to consider the Faster R-CNN as a function that generates realizations from the probability distribution of the pedestrian to be detected in the target sequence. Our contribution is to approximate this target probability distribution with a set of samples and an associated specialized Faster R-CNN estimated in a sequential Bayesian filter framework. The resulting algorithm is compared to the state of the art scene specialization methods on several challenging datasets. The results are very promising.