{'fraction_non_alphanumeric': 0.014249790444258172, 'fraction_numerical': 0.0008382229673093043, 'mean_word_length': 5.03030303030303, '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 HHMM-Based Approach for Robust Fall Detection

Automatic detection of a falling person in video sequences is an important part of future pervasive home monitoring systems. We propose here a robust method to achieve this goal. Motion is modeled by a hierarchical hidden Markov model (HHMM) whose first layer states are related to the orientation of the tracked person. Finding a consistent way for robustly linking the observation vector to the human poses is the heart of our contribution. In that sense, we carefully study the relationship between angles in the 3D world and their projection onto the image plane. After performing an initial image metric rectification, we derive theoretical properties making it possible to bound the error angle introduced by the image formation process for a standing posture. This allows us to confidently identify other poses as "non-standing" ones, and thus to robustly analyze pose sequences against a given motion model. Several results illustrate the efficiency of the algorithm by pointing out its ability to accurately recognize a person falling down from another walking or sitting, as well as its capacity to run in an unspecified configuration