Video cameras are pervasively deployed in city scale for public good or
community safety (i.e. traffic monitoring or suspected person tracking).
However, analyzing large scale video feeds in real time is data intensive and
poses severe challenges to network and computation systems today. We present
CrossRoI, a resource-efficient system that enables real time video analytics at
scale via harnessing the videos content associations and redundancy across a
fleet of cameras. CrossRoI exploits the intrinsic physical correlations of
cross-camera viewing fields to drastically reduce the communication and
computation costs. CrossRoI removes the repentant appearances of same objects
in multiple cameras without harming comprehensive coverage of the scene.
CrossRoI operates in two phases - an offline phase to establish cross-camera
correlations, and an efficient online phase for real time video inference.
Experiments on real-world video feeds show that CrossRoI achieves 42% - 65%
reduction for network overhead and 25% - 34% reduction for response delay in
real time video analytics applications with more than 99% query accuracy, when
compared to baseline methods. If integrated with SotA frame filtering systems,
the performance gains of CrossRoI reach 50% - 80% (network overhead) and 33% -
61% (end-to-end delay).

CrossRoI 是一种资源有效的系统，通过利用视频内容的关联性和冗余性在规模上实现实时视频分析，其中包括离线相位建立跨相机关系的决策，以及高效在线相位的实时视频推理，同时比基线方法具有更高的查询准确性和较高的性能收益。

跨摄像机兴趣区域优化：大规模高效实时视频分析

CrossRoI: Cross-camera Region of Interest Optimization for Efficient  Real Time Video Analytics at Scale

Mostexistingpersonre-identification(re-id)methods relyon supervised model
learning on per-camera-pair manually labelled pairwise training data. This
leads to poor scalability in practical re-id deployment due to the lack of
exhaustive identity labelling of image positive and negative pairs for every
camera pair. In this work, we address this problem by proposing an unsupervised
re-id deep learning approach capable of incrementally discovering and
exploiting the underlying re-id discriminative information from automatically
generated person tracklet data from videos in an end-to-end model optimisation.
We formulate a Tracklet Association Unsupervised Deep Learning (TAUDL)
framework characterised by jointly learning per-camera (within-camera) tracklet
association (labelling) and cross-camera tracklet correlation by maximising the
discovery of most likely tracklet relationships across camera views. Extensive
experiments demonstrate the superiority of the proposed TAUDL model over the
state-of-the-art unsupervised and domain adaptation re- id methods using six
person re-id benchmarking datasets.

本论文提出了一种基于轨迹的无监督深度学习方法，通过轨迹关联和跨摄像头相关性最大化来发现并利用单个摄像机和跨摄像机之间的人物识别信息，实现了更好的人物再识别效果。