Rinegan: A Scalable Image Processing Architecture for Large Scale Surveillance Applications

Rinegan: A Scalable Image Processing Architecture for Large Scale Surveillance Applications

Blog Article

Image processing is widely used in intelligent robots, significantly improving the surveillance capabilities of smart buildings, Q-K Foot Board Extensions 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 Hair Masks 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.