When performance matters: running a real-time computer vision pipeline| PyData Eindhoven 2021
We have been developing an automated camera solution, in which we use video input, neural networks for player detection, sport-specific decision making and a PTZ-camera to deliver an automated livestreaming application. The complete pipeline has to work in real-time with a maximum delays of 300ms. Within the 300ms we have to fetch the video streams, run several neural networks, apply business rules and control the PTZ. Over the past year, we have had to refactor code, remove complete modules or change python packages due to delay constraints. During the talk we will show the insights we gained during the process. Specifically, we will go into:
(a) overall architecture of the pipeline.
(b) system requirements.
(c) implementing a logging module to better identify the bottlenecks in the applications.
(d) use high performance queues for communication.
Jelmer Wilhelm: Background in industrial engineering, data analysis, machine learning and artificial intelligence. Currently working at Whitebox Data Science; a company specialized in custom software and data analysis for logistics companies. Also working at Eyedle; a company specialized in computer vision software.
Github: https://github.com/j-wilhelm/
LinkedIn: / jelmer-wilhelm
PyData Eindhoven 2021
Website: https://pydata.org/eindhoven2021/
Twitter: / pydataeindhoven
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