CamTuner: Reinforcement-Learning based System for Camera Parameter Tuning to enhance Analytics

by   Sibendu Paul, et al.

Complex sensors like video cameras include tens of configurable parameters, which can be set by end-users to customize the sensors to specific application scenarios. Although parameter settings significantly affect the quality of the sensor output and the accuracy of insights derived from sensor data, most end-users use a fixed parameter setting because they lack the skill or understanding to appropriately configure these parameters. We propose CamTuner, which is a system to automatically, and dynamically adapt the complex sensor to changing environments. CamTuner includes two key components. First, a bespoke analytics quality estimator, which is a deep-learning model to automatically and continuously estimate the quality of insights from an analytics unit as the environment around a sensor change. Second, a reinforcement learning (RL) module, which reacts to the changes in quality, and automatically adjusts the camera parameters to enhance the accuracy of insights. We improve the training time of the RL module by an order of magnitude by designing virtual models to mimic essential behavior of the camera: we design virtual knobs that can be set to different values to mimic the effects of assigning different values to the camera's configurable parameters, and we design a virtual camera model that mimics the output from a video camera at different times of the day. These virtual models significantly accelerate training because (a) frame rates from a real camera are limited to 25-30 fps while the virtual models enable processing at 300 fps, (b) we do not have to wait until the real camera sees different environments, which could take weeks or months, and (c) virtual knobs can be updated instantly, while it can take 200-500 ms to change the camera parameter settings. Our dynamic tuning approach results in up to 12 accuracy of insights from several video analytics tasks.


APT: Adaptive Perceptual quality based camera Tuning using reinforcement learning

Cameras are increasingly being deployed in cities, enterprises and roads...

Elixir: A system to enhance data quality for multiple analytics on a video stream

IoT sensors, especially video cameras, are ubiquitously deployed around ...

Why is the video analytics accuracy fluctuating, and what can we do about it?

It is a common practice to think of a video as a sequence of images (fra...

Making 360^∘ Video Watchable in 2D: Learning Videography for Click Free Viewing

360^∘ video requires human viewers to actively control "where" to look w...

Control and Evaluation of Event Cameras Output Sharpness via Bias

Event cameras also known as neuromorphic sensors are relatively a new te...

Virtual Sensor Middleware: Managing IoT Data for the Fog-Cloud Platform

This paper introduces the Virtual Sensor Middleware (VSM), which facilit...

Dynamic Storyboard Generation in an Engine-based Virtual Environment for Video Production

Amateurs working on mini-films and short-form videos usually spend lots ...

Please sign up or login with your details

Forgot password? Click here to reset