{'fraction_non_alphanumeric': 0.020618556701030927, 'fraction_numerical': 0.0, 'mean_word_length': 5.773255813953488, '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}}
Pedestrian detection using background subtraction assisted Support Vector Machine

This paper achieves fast and effective pedestrian detection using Histogram of Oriented Gradient (HOG) descriptor based Support Vector Machine (SVM). A novel approach taking advantage of CodeBook background subtraction(CBBS) is presented in this paper to produce pedestrian samples for SVM. HOG features of the samples are extracted to train Linear and RBF SVM classifiers offline. The classifier is adopted as pedestrian detector in online real-time video sequence detection. The influence of various ratios of positive and negative training sets on detector's performance is carefully investigated. We also compare Linear and RBF SVM in experiments as well. It is concluded that robust feature extraction, proper positive and negative training sample construction, and fine kernel function are crucial for good classification results. Experiments prove that our detector obtains a reliable detection result, which not only satisfies real-time requirement, and is robust against pedestrian appearance and pose variations, illumination changes, background changes, shadows and etc.