CAMEL: An Adaptive Camera with Embedded Machine Learning-Based Sensor Parameter Control

Today's cameras are designed to approximate what they observe in a manner that preserves entropy. However, time critical autonomous applications such as autonomous driving, surveillance and defense systems require task critical information at the highest quality. With rapid advances in frame rates and resolutions, observing scenes at the highest quality raises concerns for the transmission bandwidth. In this paper, we introduce a new paradigm of smart camera that captures only task-critical information at the highest quality. Embedded deep neural network (DNN) algorithms within the camera enhance quality of information through real-time control of sensor parameters. We show the hardware feasibility of this camera by demonstrating a 3D-stacked architecture with a Digital Pixel Sensor (DPS). We demonstrate a number of high-level vision applications that benefit through this task-guided control including object detection, object tracking and activity recognition. Finally, we present the unique challenges faced created as a result of feedback and show how software/hardware innovations can be used to mitigate them.