Scalable Big Data Pipeline for Video Stream Analytics Over Commodity Hardware

A huge amount of data in the form of videos and images is being produced owning to advancements in sensor technology. Use of low performance commodity hardware coupled with resource heavy image processing and analyzing approaches to infer and extract actionable insights from this data poses a bottleneck for timely decision making. Current approach of GPU assisted and cloud-based architecture video analysis techniques give significant performance gain, but its usage is constrained by financial considerations and extremely complex architecture level details. In this paper we propose a data pipeline system that uses open-source tools such as Apache Spark, Kafka and OpenCV running over commodity hardware for video stream processing and image processing in a distributed environment. Experimental results show that our proposed approach eliminates the need of GPU based hardware and cloud computing infrastructure to achieve efficient video steam processing for face detection with increased throughput, scalability and better performance.

Automated summary

Advancements in sensor technology have led to the production of a huge amount of video and image data. However, the use of low-performance hardware and resource-heavy image processing approaches poses a bottleneck in extracting actionable insights. In this paper, a data pipeline system is proposed that utilizes open-source tools and commodity hardware for video stream processing and image processing in a distributed environment.