Ilya Brailovskiy, Principal Engineer at Amazon Lab126, presents the "How Image Sensor and Video Compression Parameters Impact Vision Algorithms" tutorial at the May 2017 Embedded Vision Summit.
Recent advances in deep learning algorithms have brought automated object detection and recognition to human accuracy levels on various test datasets. But algorithms that work well on an engineer’s PC often fail when deployed as part of a complete embedded system. In this talk, Brailovskiy examines some of the key embedded vision system elements that can degrade the performance of vision algorithms.
For example, in many systems video is compressed, transmitted, and then decompressed before being presented to vision algorithms. Not surprisingly, video encoding parameters, such as bit rate, can have a significant impact on vision algorithm accuracy. Similarly, image sensor parameters can have a profound effect on the nature of the images captured, and therefore on the performance of vision algorithms. He explores how image sensor and video compression parameters impact vision algorithm performance, and discusses methods for selecting the best parameters to aid vision algorithm accuracy.