Understanding How Video Quality Affects Object Detection Algorithms
Miloud Aqqa, Pranav Mantini, Shishir K. Shah · 2019
Video quality is an important practical challenge that is often overlooked in the design of automated video surveillance systems. Commonly, visual intelligent systems are trained and tested on high quality image datasets, yet in practical video surveillance applications the video frames can not be assumed to be of high quality due to video encoding, transmission and decoding. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In this paper we provide an evaluation of 4 state-of-the-art deep neural network models for object detection under various levels of video compression. We show that the existing detectors are susceptible to quality distortions stemming from compression artifacts during video acquisition. These results enable future work in developing object detectors that are more robust to video quality.