{'fraction_non_alphanumeric': 0.01879194630872483, 'fraction_numerical': 0.0, 'mean_word_length': 4.920634920634921, '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 banner ads searching and counting system for sports videos

The ads exposure frequency in a TV is very important for the pricing of the advertising. If we count this exposure frequency by person, it will be very time consuming and error-prone. In this paper, we propose an approach and design a system for searching and counting the number of banner ads appearing in sports videos. The searching proceeds by matching feature points from pre-specified sample ads using the color histogram and the SURF (Speeded-Up Robust Features) algorithm. This searching approach can robustly identify objects among scaling and partial occlusion while achieving near real-time performance. This system worked effectively in our experiments using real videos.