MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark
Sanghyun Woo Kwanyong Park Inkyu Shin Myungchul Kim In So Kweon
Multi-target multi-camera tracking is a crucial task that involves
identifying and tracking individuals over time using video streams from
multiple cameras. This task has practical applications in various fields, such
as visual surveillance, crowd behavior analysis, and anomaly detection.
However, due to the difficulty and cost of collecting and labeling data,
existing datasets for this task are either synthetically generated or
artificially constructed within a controlled camera network setting, which
limits their ability to model real-world dynamics and generalize to diverse
camera configurations. To address this issue, we present MTMMC, a real-world,
large-scale dataset that includes long video sequences captured by 16
multi-modal cameras in two different environments - campus and factory - across
various time, weather, and season conditions. This dataset provides a
challenging test-bed for studying multi-camera tracking under diverse
real-world complexities and includes an additional input modality of spatially
aligned and temporally synchronized RGB and thermal cameras, which enhances the
accuracy of multi-camera tracking. MTMMC is a super-set of existing datasets,
benefiting independent fields such as person detection, re-identification, and
multiple object tracking. We provide baselines and new learning setups on this
dataset and set the reference scores for future studies. The datasets, models,
and test server will be made publicly available.