{'fraction_non_alphanumeric': 0.01327433628318584, 'fraction_numerical': 0.0, 'mean_word_length': 4.984126984126984, '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}}
Scale and Rotation Invariant Approach to Tracking Human Body Part Regions in Videos

We propose a novel scale and rotation invariant method to track a human subject's body part regions in cluttered videos. The proposed method optimizes the assembly of body part region proposals with the spatial and temporal constraints of a human body plan. This approach is invariant to the object scale and rotation changes. To enable scale and rotation invariance, the human body part graph of the proposed method has to be loopy, efficiently optimizing the body part region assembly is a great challenge. We propose a dynamic programming method to solve the problem. We devise a method that finds N-best whole body configurations from loopy structures in each video frame using dynamic programming. The N-best configurations are then used to construct trellises with which we track human body part regions by finding shortest paths on the trellises. Our experiments on a variety of videos show that the proposed method is efficient, accurate and robust against object appearance variations, scale and rotation changes and background clutter.