A No-Reference Image Quality Model for Object Detection on Embedded Cameras

Abstract

Automatic video analysis tools are an indispensable component in imaging applications. Object detection, the first and the most important step for automatic video analysis, is implemented in many embedded cameras. The accuracy of object detection relies on the quality of images that are processed. This paper proposes a new image quality model for predicting the performance of object detection on embedded cameras. A video data set is constructed that considers different factors for quality degradation in the imaging process, such as reduced resolution, noise, and blur. The performances of commonly used low-complexity object detection algorithms are obtained for the data set. A no-reference regression model based on a bagging ensemble of regression trees is built to predict the accuracy of object detection using observable features in an image. Experimental results show that the proposed model provides more accurate predictions of image quality for object detection than commonly known image quality measures.