{'fraction_non_alphanumeric': 0.034653465346534656, 'fraction_numerical': 0.01782178217821782, 'mean_word_length': 4.982248520710059, '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}}
A novel algorithm for real time human classifier using Skin Colour identification

This paper presents a novel approach for human classification based on skin colour identification technique. Next the same classifier is extended for the path planning and control of autonomous robots. The techniques for obstacle identification used in this work are Skin Colour Based (SCB), Pixel Count Based (PCB), Correlation Coefficient Based (CCB) and Histogram methods. In real- time obstacle detection, the Pixel Count Based (PCB) algorithm, Correlation Coefficient Based (CCB) and Skin Colour Based (SCB) algorithm are used. In this work CCB and PCB methods compare the similarities between two objects but SCB algorithm is to identify whether the tracked object is human or nonhuman in real time. Real time experimental results demonstrated the accuracy of CCB, PCB and SCB algorithms are 87.5% and 88.8% and 90.9% respectively and the time of CCB, PCB and SCB algorithms are 6.27sec, 6.50sec and 8.67sec respectively.