ABSTRACT
Autonomous machine vision is a powerful tool to address challenges in multiple domains including national security (for example, video surveillance), health care (for example, patient monitoring), and transportation (for example, autonomous vehicles). Distributed vision, where multiple cameras observe a specific geographic area 24/7, enables smart understanding of events in a physical environment with minimal human intervention. We observe that the cloud paradigm alone does not offer a pathway to real-time distributed vision processing. With potentially thousands of cameras, hundreds of gigabytes data per second needs to be transferred to the cloud, saturating the bandwidth of the network. More importantly, vision applications are inherently latency-critical with a high demand for real-time scene analysis (for example, feature extraction and object tracking). To meet latency requirements, computation - including both processing of raw video streams to identify objects, and analytics on this data, needs to be brought to the edge of the network. While object recognition may be done locally at the end node (next to the camera), vision analytics requires access to data generated across different nodes. For example, a subject of interest may need to be tracked across multiple cameras to identify the nature of activities. This creates a need for a low latency distributed data store communicating over a dynamic communication network (most often wireless), to be implemented at the edge. Moreover, the data store must be able to address the limited storage at the end nodes (typically gigabytes). Additionally, privacy and security are prime concerns in the design of such a distributed edge storage.
- A. Rajaraman and J. D. Ullman, Mining of Massive Datasets. New York, NY, USA: Cambridge University Press, 2011. Google ScholarCross Ref
- "RYU SDN Framework," https://osrg.github.io/ryu-book/en/Ryubook.pdf, accessed: 2017-07-12.Google Scholar
- "CBCL Streetscenes Challenge Framework," http://web.archive.org/web/20080207010024/http://www.808multimedia.com/winnt/kernel.htm, accessed: 2010-09-30.Google Scholar
- C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," CoRR, vol. abs/1512.00567, 2015. [Online]. Available: http://arxiv.org/abs/1512.00567Google Scholar
Recommendations
Stereo vision using two PTZ cameras
The research of traditional stereo vision is mainly based on static cameras. As PTZ (Pan-Tilt-Zoom) cameras are able to obtain multi-view-angle and multi-resolution information, they have received more and more concern in both research and real ...
The calibration method for stereoscopic vision system
Stereoscopic vision systems are used not only in visual design computing but also in many other applications. In stereoscopic vision, an important property is the accuracy of three-dimensional reconstruction. This property depends considerably on the ...
Comments