{'fraction_non_alphanumeric': 0.03000882612533098, 'fraction_numerical': 0.0, 'mean_word_length': 4.968421052631579, '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 skeleton-based pairwise curve matching scheme for people tracking in a multi-camera environment

In this paper, we propose a pairwise curve matching scheme in a multi-camera environment to handle the mis-tracking issue caused by occlusion problem happened in a single camera. According to the skeleton/joints of a human subject analyzed from a depth camera (e.g., Kinect), based the foot points (joints) used for people tracking in a field of view, we apply homography transformation to project the foot points from different views to a virtual bird's eye view, using Kalman filter to achieve people tracking with a pairwise curve matching. The contribution of this paper is trifold: (a) the proposed pair-wise curve matching scheme can handle the occlusion problem happened in one of the cameras, (b) the complexity of the proposed scheme is low and affordable to be implemented in a realtime application, and (c) the implementation on a Kinect camera can provide satisfactory tracking results in a bright or extremely dark environment due to the skeletons/joints analyzed by the coded structured light-based infrared (IR) sensor.