基于分布式流式计算的生猪养殖视频监测分析系统

Video Monitoring and Analysis System for Pig Breeding Based on Distributed Flow Computing

摘要:

基于分布式流式计算框架，提出了节点资源调度器算法，构建了可插拔的分布式流式实时计算模型，研究开发了生猪养殖视频监测分析系统。系统实现了规模化生猪养殖视频流数据采集、任务调度、实时计算、可插拔式扩展和结果展示的功能。在由1个主节点和3个从节点构成的集群下，采用改进混合高斯模型的背景更新方式，实现集群下多摄像头多目标的实时检测。平均处理速度比传统混合高斯模型提高了29.00%，平均检测率为79.00%，平均误检率比传统混合高斯模型降低了70.96%。测试结果表明，可插拔分布式流式实时计算模型具有较好的可扩展性，视频流处理算法速度和实时性得到了提升，具有较高的检测率和较低的误检率。

Abstract:

With the rapid development of computer technology, it is possible to process multi-type and mass data in real-time. In order to overcome the problems of distributed streaming computing in processing a large number of pig video streaming data when the delay was high and with poor scalability problems, a node resource scheduler algorithm was proposed, and a pluggable distributed real-time flow computation model was constructed. A system of video monitoring and analysis for pig breeding based on distributed flow calculation was developed. The system implemented the functions of pig video stream data acquisition, task scheduling, real-time calculation, pluggable expansion and result display. The test cluster consisted of a master node and three slave nodes. Under the cluster, the background refreshing method of improved hybrid Gaussian model was adopted to realize the multi-camera and multi-target detection in real-time. The average processing rate was 29.00% higher than the traditional mixed Gaussian model, the average detection rate was 79.00%, and the average false detection rate was 70.96% lower than that of the traditional mixed Gaussian model. The results showed that the pluggable distributed streaming real-time computing model had good scalability and low latency. The improved hybrid Gaussian model algorithm had high detection rate and low false detection rate.