{'fraction_non_alphanumeric': 0.019961977186311788, 'fraction_numerical': 0.0, 'mean_word_length': 5.540372670807454, '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}}
Trajectory Association and Fusion across Partially Overlapping Cameras

We present a novel unsupervised inter-camera trajectory correspondence algorithm that does not require prior knowledge of the camera placement. The approach consists of three steps, namely association, fusion and linkage. For association, local trajectory pairs corresponding to the same physical object are estimated using multiple spatio-temporal features on a common ground-plane. To disambiguate spurious associations, we employ a hybrid approach that utilizes the matching results on the image- and ground-plane. The trajectory segments after association are fused by adaptive averaging. Finally, linkage integrates segments and generates a single trajectory of an object across the entire observed area. We evaluated the performance of the proposed approach on a simulated and two real scenarios with simultaneous moving objects observed by multiple cameras and compared it with state-of-the-art algorithms. Convincing results are observed in favor of the proposed approach.