{'fraction_non_alphanumeric': 0.022900763358778626, 'fraction_numerical': 0.0, 'mean_word_length': 5.25, '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}}
VISION-BASED PEDESTRIAN DETECTION USING HAAR-LIKE FEATURES

This paper describes a vision-based pedestrian detection system for robots, and autonomous vehicles. For that purpose the Haar-like features were used to discriminate pedestrians. Those features were used as input in a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields an extremely efficient classifier. The proposed system can run in real-time applications achieving good detection rates.