Enabling versatile analysis of large scale traffic video data with deep learning and HiveQL

While monocular roadside cameras have been widely deployed and used to monitor traffic conditions across the United States, the analysis of those video data are commonly implemented either manually or through commercial applications tailor-made for specific tasks. The goal of this project is to develop an efficient system that can meet dynamic content based video analysis needs and scale to large scale traffic camera video data. The proposed system utilizes deep learning methods to recognize objects in the video data. That information can then be processed and analyzed through an analysis layer implemented using Spark and Hive. The analysis layer supports HiveQL, which enables end users to conduct sophisticated analysis with customized queries. In this paper, we present the implementation of this prototype application in details. The application can utilize both GPU and multiple CPUs to accelerate its computation. We evaluated its performance and scalability with different hardware and parameter settings, including Intel Knights Landing, Intel Skylake, Nvidia K40 GPU, and Nvidia P100 GPU, for object recognition. To demonstrate its versatile, we show two practical use case examples: counting moving vehicles and identifying scenes including pedestrians and vehicles. We show the accuracy of the system by comparing vehicular counts produced by the analysis with manually annotated results. The comparison shows our methods can achieve over eighty percent accuracy comparing to manual results.