为了解决海量交通视频数据的监控和分析问题，本文对Hadoop大数据背景下的交通视频监控技术进行了深入研究，提出了基于交通视频数据的异常检测算法的设计方案，实现了交通数据的实时更新和异常分析，同时针对海量交通监控视频，设计了基于Hadoop组件MapReduce的并行实现算法，并通过浙江省某市的实际交通数据验证算法的有效性和准确性。经过实验证明，本文算法可以有效计算出交通拥堵情况和异常情况，相对于传统方案，本文方案可以聚焦10 min范围内的时间粒度对交通情况进行实时分析，相对于传统的分布式计算模型，本文的方案10 min延迟可以控制在2.1 s,比传统方案延迟降低了81%,基本满足交通视频监控的实时和细颗粒度等要求。

In order to solve the problem of monitoring and analyzing massive traffic video data, the in-depth research on traffic video surveillance technology in the context of hadoop big data is conducted, and a design scheme of anomaly jam detection algorithm is proposed based on traffic video data to realize traffic real-time data update and anomaly analysis. At the same time, for the massive traffic monitoring video, a parallel implementation algorithm is designed based on Hadoop component MapReduce. Finally, the effectiveness and accuracy of the algorithm is verified by actual traffic data of a city in Zhejiang Province. The algorithm in this paper can effectively calculate the traffic congestion and abnormal conditions. Compared with the traditional scheme, this scheme can focus on the time granularity in the range of 10 min to analyze the traffic situation in real time. Compared with the traditional distributed computing model, the 10 minute delay of this scheme can be controlled at 2.1 s, which is 81% lower than the traditional scheme, which basically meets the real-time, fine-grained requirements for traffic video surveillance requirements.