{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T04:47:25Z","timestamp":1768106845259,"version":"3.49.0"},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"6","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:p>Discovering co-movement patterns from urban-scale video data sources has emerged as an attractive topic. This task aims to identify groups of objects that travel together along a common route, which offers effective support for government agencies in enhancing smart city management. However, the previous work has made a strong assumption on the accuracy of recovered trajectories from videos and their co-movement pattern definition requires the group of objects to appear across consecutive cameras along the common route. In practice, this often leads to missing patterns if a vehicle is not correctly identified from a certain camera due to object occlusion or vehicle mis-matching.<\/jats:p>\n          <jats:p>To address this challenge, we propose a relaxed definition of co-movement patterns from video data, which removes the consecutiveness requirement in the common route and accommodates a certain number of missing captured cameras for objects within the group. Moreover, a novel enumeration framework called Max-Growth is developed to efficiently retrieve the relaxed patterns. Unlike previous filter-and-refine frameworks comprising both candidate enumeration and subsequent candidate verification procedures, MaxGrowth incurs no verification cost for the candidate patterns. It treats the co-movement pattern as an equivalent sequence of clusters, enumerating candidates with increasing sequence length while avoiding the generation of any false positives. Additionally, we also propose two effective pruning rules to efficiently filter the non-maximal patterns. Extensive experiments are conducted to validate the efficiency of MaxGrowth and the quality of its generated co-movement patterns. Our MaxGrowth runs up to two orders of magnitude faster than the baseline algorithm. It also demonstrates high accuracy in real video dataset when the trajectory recovery algorithm is not perfect.<\/jats:p>","DOI":"10.14778\/3725688.3725710","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T14:19:21Z","timestamp":1756477161000},"page":"1839-1851","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Mining Platoon Patterns from Traffic Videos"],"prefix":"10.14778","volume":"18","author":[{"given":"Yijun","family":"Bei","sequence":"first","affiliation":[{"name":"Zhejiang University, China"}]},{"given":"Teng","family":"Ma","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]},{"given":"Dongxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Blockchain and Data Security, Zhejiang University"}]},{"given":"Sai","family":"Wu","sequence":"additional","affiliation":[{"name":"Hangzhou High-Tech Zone (Binjiang), Institute of Blockchain and Data Security"}]},{"given":"Kian-Lee","family":"Tan","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]},{"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, China"}]}],"member":"320","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Yijun Bei Teng Ma Dongxiang Zhang Sai Wu Kian-Lee Tan and Gang Chen. 2024. Mining Platoon Patterns from Traffic Videos. arXiv:2412.20177 [cs.CV] https:\/\/arxiv.org\/abs\/2412.20177"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comgeo.2007.10.003"},{"key":"e_1_2_1_3_1","volume-title":"2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 164\u2013176","author":"Chao Daren","year":"2023","unstructured":"Daren Chao, Yueting Chen, Nick Koudas, and Xiaohui Yu. 2023. Track merging for effective video query processing. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 164\u2013176."},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.14778\/3339490.3339502","article-title":"Real-time distributed co-movement pattern detection on streaming trajectories","volume":"12","author":"Chen Lu","year":"2019","unstructured":"Lu Chen, Yunjun Gao, Ziquan Fang, Xiaoye Miao, Christian S Jensen, and Chenjuan Guo. 2019. Real-time distributed co-movement pattern detection on streaming trajectories. Proceedings of the VLDB Endowment 12, 10 (2019), 1208\u20131220.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_2_1_5_1","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1109\/TKDE.2015.2506556","article-title":"Efficient Metric Indexing for Similarity Search and Similarity Joins","volume":"29","author":"Chen Lu","year":"2017","unstructured":"Lu Chen, Yunjun Gao, Xinhan Li, Christian S Jensen, and Gang Chen. 2017. Efficient Metric Indexing for Similarity Search and Similarity Joins. IEEE Transactions on Knowledge and Data Engineering 29, 3 (2017), 556\u2013571.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_1_6_1","volume-title":"Mot20: A benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003","author":"Dendorfer Patrick","year":"2020","unstructured":"Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, and Laura Leal-Taix\u00e9. 2020. Mot20: A benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003 (2020)."},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","first-page":"787","DOI":"10.14778\/3192965.3192970","article-title":"Ul-TraMan: A unified platform for big trajectory data management and analytics","volume":"11","author":"Ding Xin","year":"2018","unstructured":"Xin Ding, Lu Chen, Yunjun Gao, Christian S Jensen, and Hujun Bao. 2018. Ul-TraMan: A unified platform for big trajectory data management and analytics. Proceedings of the VLDB Endowment 11, 7 (2018), 787\u2013799.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_2_1_8_1","unstructured":"Martin Ester Hans-Peter Kriegel J\u00f6rg Sander Xiaowei Xu et al. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd Vol. 96. 226\u2013231."},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.14778\/3025111.3025114","article-title":"A general and parallel platform for mining co-movement patterns over large-scale trajectories","volume":"10","author":"Fan Qi","year":"2016","unstructured":"Qi Fan, Dongxiang Zhang, Huayu Wu, and Kian-Lee Tan. 2016. A general and parallel platform for mining co-movement patterns over large-scale trajectories. Proceedings of the VLDB Endowment 10, 4 (2016), 313\u2013324.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems. 35\u201342","author":"Gudmundsson Joachim","year":"2006","unstructured":"Joachim Gudmundsson and Marc Van Kreveld. 2006. Computing longest duration flocks in trajectory data. In Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems. 35\u201342."},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. 576\u2013577","author":"He Yuhang","year":"2020","unstructured":"Yuhang He, Jie Han, Wentao Yu, Xiaopeng Hong, Xing Wei, and Yihong Gong. 2020. City-scale multi-camera vehicle tracking by semantic attribute parsing and cross-camera tracklet matching. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. 576\u2013577."},{"key":"e_1_2_1_12_1","volume-title":"2008 IEEE 24th International Conference on Data Engineering. IEEE, 1457\u20131459","author":"Jeung Hoyoung","year":"2008","unstructured":"Hoyoung Jeung, Heng Tao Shen, and Xiaofang Zhou. 2008. Convoy queries in spatio-temporal databases. In 2008 IEEE 24th International Conference on Data Engineering. IEEE, 1457\u20131459."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/1453856.1453971"},{"key":"e_1_2_1_14_1","volume-title":"Advances in Spatial and Temporal Databases: 9th International Symposium, SSTD 2005, Angra dos Reis, Brazil, August 22\u201324, 2005. Proceedings 9. Springer, 364\u2013381","author":"Kalnis Panos","year":"2005","unstructured":"Panos Kalnis, Nikos Mamoulis, and Spiridon Bakiras. 2005. On discovering moving clusters in spatio-temporal data. In Advances in Spatial and Temporal Databases: 9th International Symposium, SSTD 2005, Angra dos Reis, Brazil, August 22\u201324, 2005. Proceedings 9. Springer, 364\u2013381."},{"key":"e_1_2_1_15_1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.datak.2015.02.001","article-title":"Efficient mining of platoon patterns in trajectory databases","volume":"100","author":"Li Yuxuan","year":"2015","unstructured":"Yuxuan Li, James Bailey, and Lars Kulik. 2015. Efficient mining of platoon patterns in trajectory databases. Data & Knowledge Engineering 100 (2015), 167\u2013187.","journal-title":"Data & Knowledge Engineering"},{"key":"e_1_2_1_16_1","first-page":"1","article-title":"Swarm: Mining relaxed temporal moving object clusters","volume":"3","author":"Li Zhenhui","year":"2010","unstructured":"Zhenhui Li, Bolin Ding, Jiawei Han, and Roland Kays. 2010. Swarm: Mining relaxed temporal moving object clusters. Proceedings of the VLDB Endowment 3, 1\u20132 (2010), 723\u2013734.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1089\u20131098","author":"Liu Yiyang","year":"2021","unstructured":"Yiyang Liu, Hua Dai, Bohan Li, Jiawei Li, Geng Yang, and Jun Wang. 2021. ECMA: an efficient convoy mining algorithm for moving objects. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1089\u20131098."},{"key":"e_1_2_1_18_1","doi-asserted-by":"crossref","first-page":"948","DOI":"10.14778\/3329772.3329773","article-title":"k\/2-hop: fast mining of convoy patterns with effective pruning","volume":"12","author":"Orakzai Faisal Moeen","year":"2019","unstructured":"Faisal Moeen Orakzai, Toon Calders, and Torben Bach Pedersen. 2019. k\/2-hop: fast mining of convoy patterns with effective pruning. Proceedings of the VLDB Endowment 12, 9 (2019), 948\u2013960.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_2_1_19_1","volume-title":"European conference on computer vision. Springer, 17\u201335","author":"Ristani Ergys","year":"2016","unstructured":"Ergys Ristani, Francesco Solera, Roger Zou, Rita Cucchiara, and Carlo Tomasi. 2016. Performance measures and a data set for multi-target, multi-camera tracking. In European conference on computer vision. Springer, 17\u201335."},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 20993\u201321002","author":"Sun Peize","year":"2022","unstructured":"Peize Sun, Jinkun Cao, Yi Jiang, Zehuan Yuan, Song Bai, Kris Kitani, and Ping Luo. 2022. Dancetrack: Multi-object tracking in uniform appearance and diverse motion. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 20993\u201321002."},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 8797\u20138806","author":"Tang Zheng","year":"2019","unstructured":"Zheng Tang, Milind Naphade, Ming-Yu Liu, Xiaodong Yang, Stan Birchfield, Shuo Wang, Ratnesh Kumar, David Anastasiu, and Jenq-Neng Hwang. 2019. Cityflow: A city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 8797\u20138806."},{"key":"e_1_2_1_22_1","volume-title":"Proceedings of the 27th Annual International Conference on Mobile Computing and Networking. 188\u2013200","author":"Tong Panrong","year":"2021","unstructured":"Panrong Tong, Mingqian Li, Mo Li, Jianqiang Huang, and Xiansheng Hua. 2021. Large-scale vehicle trajectory reconstruction with camera sensing network. In Proceedings of the 27th Annual International Conference on Mobile Computing and Networking. 188\u2013200."},{"key":"e_1_2_1_23_1","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.14778\/3236187.3236211","article-title":"A unified approach to route planning for shared mobility","volume":"11","author":"Tong Yongxin","year":"2018","unstructured":"Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Jieping Ye, and Ke Xu. 2018. A unified approach to route planning for shared mobility. Proceedings of the VLDB Endowment 11, 11 (2018), 1633.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_2_1_24_1","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1109\/TMM.2022.3140919","article-title":"Split and connect: A universal tracklet booster for multi-object tracking","volume":"25","author":"Wang Gaoang","year":"2022","unstructured":"Gaoang Wang, Yizhou Wang, Renshu Gu, Weijie Hu, and Jenq-Neng Hwang. 2022. Split and connect: A universal tracklet booster for multi-object tracking. IEEE Transactions on Multimedia 25 (2022), 1256\u20131268.","journal-title":"IEEE Transactions on Multimedia"},{"key":"e_1_2_1_25_1","volume-title":"Proceedings. 20th international conference on data engineering. IEEE, 79\u201390","author":"Wang Jianyong","year":"2004","unstructured":"Jianyong Wang and Jiawei Han. 2004. BIDE: Efficient mining of frequent closed sequences. In Proceedings. 20th international conference on data engineering. IEEE, 79\u201390."},{"key":"e_1_2_1_26_1","doi-asserted-by":"crossref","first-page":"29","DOI":"10.14778\/3357377.3357380","article-title":"Fast large-scale trajectory clustering","volume":"13","author":"Wang Sheng","year":"2019","unstructured":"Sheng Wang, Zhifeng Bao, J Shane Culpepper, Timos Sellis, and Xiaolin Qin. 2019. Fast large-scale trajectory clustering. Proceedings of the VLDB Endowment 13, 1 (2019), 29\u201342.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"e_1_2_1_27_1","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.datak.2005.04.006","article-title":"Efficient mining of group patterns from user movement data","volume":"57","author":"Wang Yida","year":"2006","unstructured":"Yida Wang, Ee-Peng Lim, and San-Yih Hwang. 2006. Efficient mining of group patterns from user movement data. Data & Knowledge Engineering 57, 3 (2006), 240\u2013282.","journal-title":"Data & Knowledge Engineering"},{"key":"e_1_2_1_28_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3400730","article-title":"Querying recurrent convoys over trajectory data","volume":"11","author":"Yadamjav Munkh-Erdene","year":"2020","unstructured":"Munkh-Erdene Yadamjav, Zhifeng Bao, Baihua Zheng, Farhana M Choudhury, and Hanan Samet. 2020. Querying recurrent convoys over trajectory data. ACM Transactions on Intelligent Systems and Technology (TIST) 11, 5 (2020), 1\u201324.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the 2003 SIAM international conference on data mining. SIAM, 166\u2013177","author":"Yan Xifeng","year":"2003","unstructured":"Xifeng Yan, Jiawei Han, and Ramin Afshar. 2003. Clospan: Mining: Closed sequential patterns in large datasets. In Proceedings of the 2003 SIAM international conference on data mining. SIAM, 166\u2013177."},{"key":"e_1_2_1_30_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 3096\u20133106","author":"Yang Xipeng","year":"2022","unstructured":"Xipeng Yang, Jin Ye, Jincheng Lu, Chenting Gong, Minyue Jiang, Xiangru Lin, Wei Zhang, Xiao Tan, Yingying Li, Xiaoqing Ye, et al. 2022. Box-grained reranking matching for multi-camera multi-target tracking. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 3096\u20133106."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.14778\/3632093.3632119"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3725688.3725710","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T14:22:29Z","timestamp":1756477349000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3725688.3725710"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":31,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10.14778\/3725688.3725710"],"URL":"https:\/\/doi.org\/10.14778\/3725688.3725710","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2025,2]]},"assertion":[{"value":"2025-08-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}