VATE: Edge-Cloud System for Object Detection in Real-Time Video Streams

Abstract

In the realm of edge intelligence, emerging video analytics applications are often based on resource constrained edge devices. These applications need systems which are able to provide both low-latency and high-accuracy video stream processing, such as for object detection in real-time video streams. State-of-the-art systems tackle this challenge by leveraging edge computing and cloud computing. Such edge-cloud approaches typically combine low-latency results from the edge and high accuracy results from the cloud when processing a frame of the video stream. However, the accuracy achieved so far leaves much room for improvement. Furthermore, using more accurate object detection often requires having more capable hardware. This limits the edge devices which can be used. Applications related to autonomous drones, with the drone being the edge device, give one example. A wide variety of objects needs to be detected reliably for drones to operate safely. Drones with more computing capabilities are often more expensive and suffer from short battery life, as they consume more energy. In this paper, we introduce VATE, a novel edge-cloud system for object detection in real-time video streams. An enhanced approach for edge-cloud fusion is presented, leading to improved object detection accuracy. A novel multi-object tracker is introduced, allowing VATE to run on less capable edge devices. The architecture of VATE enables it to be used when edge devices are capable of running on-device object detection frequently and when edge devices need to minimise on-device object detection to preserve battery life. Its performance is evaluated on a challenging, drone-based video dataset. The experimental results show that VATE improves accuracy by up to 27.5% compared to the state-of-the-art system, while running on less capable and cheaper hardware.