Collaborative Intelligent Cross-Camera Video Analytics at Edge: Opportunities and Challenges
Collaborative Intelligent Cross-Camera Video Analytics at Edge: Opportunities and Challenges
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DOI：
10.1145/3363347.3363360
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
H. Pasandi;T. Nadeem
中科院分区：
文献类型：
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作者：
H. Pasandi;T. Nadeem
如今，视频摄像机被大规模部署用于物理场所的空间监控（例如，智能城市背景下的监控系统）。然而，大规模摄像头的部署为分析海量数据带来了新的挑战，因为复杂深度学习技术的高计算开销在能源消耗和处理吞吐量方面对这种资源受限的边缘设备施加了令人望而却步的开销。为了解决这些限制，本文设想了网络边缘的协作智能跨摄像机视频分析范式，其中摄像机节点调整其管道（例如，推理），以整合来自其他节点内容的相关观察和共享知识。通过消除冗余时空以减少推理搜索空间的大小，以及视频节点之间的智能协作，我们讨论了与非协作基线相比，这种协作范式如何显著提高准确性、减少延迟和减少通信带宽。本文还描述了实现这一范式的主要机遇和挑战。
Nowadays, video cameras are deployed in large scale for spatial monitoring of physical places (e.g., surveillance systems in the context of smart cities). The massive camera deployment, however, presents new challenges for analyzing the enormous data, as the cost of high computational overhead of sophisticated deep learning techniques imposes a prohibitive overhead, in terms of energy consumption and processing throughput, on such resource-constrained edge devices. To address these limitations, this paper envisions a collaborative intelligent cross-camera video analytics paradigm at the network edge in which camera nodes adjust their pipelines (e.g., inference) to incorporate correlated observations and shared knowledge from other nodes' contents. By harassing redundant spatio-temporal to reduce the size of the inference search space in one hand, and intelligent collaboration between video nodes on the other, we discuss how such collaborative paradigm can considerably improve accuracy, reduce latency and decrease communication bandwidth compared to non-collaborative baselines. This paper also describes major opportunities and challenges in realizing such a paradigm.