{'fraction_non_alphanumeric': 0.02034587995930824, 'fraction_numerical': 0.001017293997965412, 'mean_word_length': 5.431372549019608, '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}}
Bidirectional motion tracking for video indexing

Motion is recognized as one of the most essential video object (VO) features in indexing video contents. Previously, we directly exploited macroblock motion vectors (MVs) of MPEG bitstream without performing full decoding to extract motion trajectories of VOs. However, the performance of VO segmentation based on MVs is constrained by the limited information extracted from the MV field, since it is quantized and irregular in nature. Therefore, an effective VO tracking scheme would be essential to compensate this limitation. In this paper, we propose a bidirectional motion tracking methodology to (i) firstly validate current segmented VOs by exploring their correlations to the VOs obtained from previous and future frames and (ii) subsequently track each validated VO using discrete Kalman filter. The proposed scheme has been well tested using MPEG-7 test materials and achieves more reliable VO motion trajectory extraction.