AUTHOR=Luo Xi , Feng Lei , Xun Hao , Zhang Yuanfei , Li Yixin , Yin Lihua TITLE=Rinegan: A Scalable Image Processing Architecture for Large Scale Surveillance Applications JOURNAL=Frontiers in Neurorobotics VOLUME=15 YEAR=2021 URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2021.648101 DOI=10.3389/fnbot.2021.648101 ISSN=1662-5218 ABSTRACT=

Image processing is widely used in intelligent robots, significantly improving the surveillance capabilities of smart buildings, industrial parks, and border ports. However, relying on the camera installed in a single robot is not enough since it only provides a narrow field of view as well as limited processing performance. Specially, a target person such as the suspect may appear anywhere and tracking the suspect in such a large-scale scene requires cooperation between fixed cameras and patrol robots. This induces a significant surge in demand for data, computing resources, as well as networking infrastructures. In this work, we develop a scalable architecture to optimize image processing efficacy and response rate for visual ability. In this architecture, the lightweight pre-process and object detection functions are deployed on the gateway-side to minimize the bandwidth consumption. Cloud-side servers receive solely the recognized data rather than entire image or video streams to identify specific suspect. Then the cloud-side sends the information to the robot, and the robot completes the corresponding tracking task. All these functions are implemented and orchestrated based on micro-service architecture to improve the flexibility. We implement a prototype system, called Rinegan, and evaluate it in an in-lab testing environment. The result shows that Rinegan is able to improve the effectiveness and efficacy of image processing.