Co-movement Pattern Mining from Videos
Dongxiang Zhang, Teng Ma, Junnan Hu, Yijun Bei, Kian-Lee Tan, Gang, Chen

TL;DR
This paper introduces a novel approach to co-movement pattern mining from surveillance videos, adapting spatial-temporal constraints, proposing new algorithms, and demonstrating efficiency and effectiveness through extensive experiments.
Contribution
It presents the first investigation into video-based co-movement pattern mining, introduces a new index (TCS-tree), and develops a sequence-ahead pruning framework for efficient pattern discovery.
Findings
The proposed methods are significantly faster than baseline algorithms.
The video-driven patterns closely match GPS-based groundtruth trajectories.
The approach effectively scales to large camera networks with 1169 cameras.
Abstract
Co-movement pattern mining from GPS trajectories has been an intriguing subject in spatial-temporal data mining. In this paper, we extend this research line by migrating the data source from GPS sensors to surveillance cameras, and presenting the first investigation into co-movement pattern mining from videos. We formulate the new problem, re-define the spatial-temporal proximity constraints from cameras deployed in a road network, and theoretically prove its hardness. Due to the lack of readily applicable solutions, we adapt existing techniques and propose two competitive baselines using Apriori-based enumerator and CMC algorithm, respectively. As the principal technical contributions, we introduce a novel index called temporal-cluster suffix tree (TCS-tree), which performs two-level temporal clustering within each camera and constructs a suffix tree from the resulting clusters.…
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Taxonomy
TopicsData Management and Algorithms · Video Analysis and Summarization · Data Mining Algorithms and Applications
