在计算约束下用于道路交通事故检测的启发式最佳调度
Hao Wu1,2, Jiahao Yang1,2, Ming-Dong Yuan1,2
1The Smart City Research Institute of China Electronics Technology Group Corporation, Shenzhen 518038, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
本研究引入了智能道路监控的最佳调度方法,以有限的资源显著提高了交通异常检测效率. 该方法动态地将资源重新分配给高性能摄像机,提高交通安全和事故预防.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 交通工程是交通工程.
背景情况:
- 道路监控视频的智能监控对于检测交通异常和道路安全至关重要.
- 当前的方法面临着高计算需求或由于周期性分析而忽视事件的挑战.
- 现有的静态资源分配模型往往导致浪费或不足.
研究的目的:
- 引入一种启发式的最佳调度方法,以在有限的计算资源下有效检测交通事件.
- 提高交通异常检测和预警系统的有效性.
- 解决传统实时分析和定期摄像机巡逻方法的局限性.
主要方法:
- 建议采用一种启发式的最佳调度方法,利用历史数据和先前的知识.
- 计算每个摄像机的加权事件特征值,以量化检测效率.
- 循环淘汰机制动态地将资源从低性能摄像机重新分配到高性能摄像机.
主要成果:
- 拟议的方法证明了交通事件检测效率的大幅提高 (40%,28%,17%,28%跨时期).
- 在平均负载程度,负载平衡程度和计算资源利用率方面,超越了现有的资源调度算法.
- 通过在中国一个主要大都会城市的案例研究来验证.
结论:
- 启发式的最佳调度方法有效地提高了在有限的计算资源的交通事件检测效率.
- 与静态分配模型相比,这种方法提供了更大的灵活性和适应性.
- 该方法成功地解决了对交通异常检测和预警系统的实际需求.
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