{'fraction_non_alphanumeric': 0.01483679525222552, 'fraction_numerical': 0.0, 'mean_word_length': 5.246913580246914, '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 study on fast object recognition based on selective visual attention system

This research focuses on the recognition of vehicle external objects using selective visual attention system. We had proposed the selective visual attention system with the feedback of recognition results. The system has three visual features such as still image feature, motion feature and blinking feature, and has intention maps for each visual feature. The intention maps have come to express existing regions of objects according to recognition feedback. Motion feature and blinking feature have strong correlation with each other. That causes a deviation of feature extraction and of intention map update. Consequently, the regions of attractive objects disappear on intention maps. Blinking feature must be independent from motion feature. We propose the blinking brand-new feature extracted by FFT applied to dozens of frames. As the results of experiments, the proposed feature can affect goodly to visual attention system.