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

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Copyright © 2021 Luo, Feng, Xun, Zhang, Li and Yin.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.