  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. Moreover, we present a sequence-ahead pruning framework based on
TCS-tree, which allows for the simultaneous leverage of all pattern constraints
to filter candidate paths. Finally, to reduce verification cost on the
candidate paths, we propose a sliding-window based co-movement pattern
enumeration strategy and a hashing-based dominance eliminator, both of which
are effective in avoiding redundant operations.
  We conduct extensive experiments for scalability and effectiveness analysis.
Our results validate the efficiency of the proposed index and mining algorithm,
which runs remarkably faster than the two baseline methods. Additionally, we
construct a video database with 1169 cameras and perform an end-to-end pipeline
analysis to study the performance gap between GPS-driven and video-driven
methods. Our results demonstrate that the derived patterns from the
video-driven approach are similar to those derived from groundtruth
trajectories, providing evidence of its effectiveness.
