  Multi-camera surveillance has been an active research topic for understanding
and modeling scenes. Compared to a single camera, multi-cameras provide larger
field-of-view and more object cues, and the related applications are multi-view
counting, multi-view tracking, 3D pose estimation or 3D reconstruction, etc. It
is usually assumed that the cameras are all temporally synchronized when
designing models for these multi-camera based tasks. However, this assumption
is not always valid,especially for multi-camera systems with network
transmission delay and low frame-rates due to limited network bandwidth,
resulting in desynchronization of the captured frames across cameras. To handle
the issue of unsynchronized multi-cameras, in this paper, we propose a
synchronization model that works in conjunction with existing DNN-based
multi-view models, thus avoiding the redesign of the whole model. Under the
low-fps regime, we assume that only a single relevant frame is available from
each view, and synchronization is achieved by matching together image contents
guided by epipolar geometry. We consider two variants of the model, based on
where in the pipeline the synchronization occurs, scene-level synchronization
and camera-level synchronization. The view synchronization step and the
task-specific view fusion and prediction step are unified in the same framework
and trained in an end-to-end fashion. Our view synchronization models are
applied to different DNNs-based multi-camera vision tasks under the
unsynchronized setting, including multi-view counting and 3D pose estimation,
and achieve good performance compared to baselines.
