Scalable distributed visual computing for line-rate video streams

The past decade has witnessed significant breakthroughs in the world of computer vision. Recent deep learning-based computer vision algorithms exhibit strong performance on recognition, detection, and segmentation. While the development of vision algorithms elicits promising applications, it also presents immense computational challenge to the underlying hardware due to its complex nature, especially when attempting to process the data at line-rate.

To this end we develop a highly scalable computer vision processing framework, which leverages advanced technologies such as Spark Streaming and OpenCV to achieve line-rate video data processing. To ensure the greatest flexibility, our framework is agnostic in terms of computer vision model, and can utilize environments with heterogeneous processing devices. To evaluate this framework, we deploy it in a production cloud computing environment, and perform a thorough analysis on the system's performance. We utilize existing real-world live video streams from Simon Fraser University to measure the number of cars entering our university campus. Further, the data collected from our experiments is being used for real-time predictions of traffic conditions on campus.