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.

中文翻译：

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Rinegan：用于大规模监控应用的可扩展图像处理架构。

图像处理广泛应用于智能机器人，显着提升智能楼宇、工业园区、边境口岸的监控能力。然而，仅仅依靠安装在单个机器人中的摄像头是不够的，因为它只能提供狭窄的视野以及有限的处理性能。特别是嫌疑人等目标人物可能出现在任何地方，在如此大规模的场景中跟踪嫌疑人需要固定摄像机和巡逻机器人的配合。这导致对数据、计算资源以及网络基础设施的需求大幅增加。在这项工作中，我们开发了一个可扩展的架构来优化图像处理效率和视觉能力的响应率。在这个架构中，网关侧部署轻量级的预处理和对象检测功能，以最大限度地减少带宽消耗。云端服务器仅接收已识别的数据，而不是整个图像或视频流，以识别特定的嫌疑人。然后云端将信息发送给机器人，机器人完成相应的跟踪任务。所有这些功能都是基于微服务架构来实现和编排的，以提高灵活性。我们实施了一个名为 Rinegan 的原型系统，并在实验室测试环境中对其进行评估。结果表明，Rinegan 能够提高图像处理的有效性和效能。然后云端将信息发送给机器人，机器人完成相应的跟踪任务。所有这些功能都是基于微服务架构来实现和编排的，以提高灵活性。我们实施了一个名为 Rinegan 的原型系统，并在实验室测试环境中对其进行评估。结果表明，Rinegan 能够提高图像处理的有效性和效能。然后云端将信息发送给机器人，机器人完成相应的跟踪任务。所有这些功能都是基于微服务架构来实现和编排的，以提高灵活性。我们实施了一个名为 Rinegan 的原型系统，并在实验室测试环境中对其进行评估。结果表明，Rinegan 能够提高图像处理的有效性和效能。