{'fraction_non_alphanumeric': 0.01726121979286536, 'fraction_numerical': 0.0011507479861910242, 'mean_word_length': 5.350364963503649, '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}}
Stereo-motion estimation for visual object tracking

Visual object tracking is a problem that has been studied by the computer community for decades. Computational stereo refers to the problem of determining 3-dimentional structure of a scene from two or more images taken from distinct viewpoints. An approach to detect stereo-moving objects with bio-cameras is presented in this thesis. Stereo and motion are detected simultaneously under a modified formulation of energy function. We extend the application from static stereo match and motion mono-camera detection to handle the whole problem together at a time, which results in a reduction of computation time and ability to handle more difficult situation. Experiments on several video-images, that showing convictive object detecting performances demonstrate its strong adaptability to stereo-motion application.