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Edge Computing Enabled Real-Time Video Analysis via Adaptive Spatial-Temporal Semantic Filtering

2024-02-29 07:42:03
Xiang Chen, Wenjie Zhu, Jiayuan Chen, Tong Zhang, Changyan Yi, Jun Cai

Abstract

This paper proposes a novel edge computing enabled real-time video analysis system for intelligent visual devices. The proposed system consists of a tracking-assisted object detection module (TAODM) and a region of interesting module (ROIM). TAODM adaptively determines the offloading decision to process each video frame locally with a tracking algorithm or to offload it to the edge server inferred by an object detection model. ROIM determines each offloading frame's resolution and detection model configuration to ensure that the analysis results can return in time. TAODM and ROIM interact jointly to filter the repetitive spatial-temporal semantic information to maximize the processing rate while ensuring high video analysis accuracy. Unlike most existing works, this paper investigates the real-time video analysis systems where the intelligent visual device connects to the edge server through a wireless network with fluctuating network conditions. We decompose the real-time video analysis problem into the offloading decision and configurations selection sub-problems. To solve these two sub-problems, we introduce a double deep Q network (DDQN) based offloading approach and a contextual multi-armed bandit (CMAB) based adaptive configurations selection approach, respectively. A DDQN-CMAB reinforcement learning (DCRL) training framework is further developed to integrate these two approaches to improve the overall video analyzing performance. Extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

Abstract (translated)

本文提出了一种名为“智能视觉设备支持边缘计算实时视频分析系统”的新颖边缘计算实时视频分析系统。该系统包括跟踪辅助物体检测模块(TAODM)和感兴趣区域模块(ROIM)。TAODM根据跟踪算法或将视频帧卸载到由物体检测模型推断的边缘服务器上进行动态决策。ROIM根据每个卸载帧的分辨率和新检测模型配置来确保分析结果及时返回。TAODM和ROIM共同交互以过滤重复的时空语义信息,以提高处理速率同时确保高视频分析精度。与大多数现有作品不同,本文研究了通过无线网络连接智能视觉设备到边缘服务器的实时视频分析系统。我们分解实时视频分析问题为卸载决策和配置选择子问题。为解决这两个子问题,我们分别引入了基于卸载的深度 Q 网络(DDQN)和基于上下文的多臂老虎机(CMAB)主动配置选择方法。进一步开发了 DDQN-CMAB 强化学习(DCRL)训练框架,以整合这两种方法并提高整体视频分析性能。进行了广泛的仿真以评估所提出的解决方案的性能,并证明了其在同类产品中的优越性。

URL

https://arxiv.org/abs/2402.18927

PDF

https://arxiv.org/pdf/2402.18927.pdf


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