An Image Quality Adjustment Framework for Object Detection on Embedded Cameras

Abstract

Automatic analysis tools are ubiquitously applied on wireless embedded cameras to extract high-level information from raw data. The quality of images may be degraded by factors such as noise and blur introduced during the sensing process, which could affect the performance of automatic analysis. Object detection is the first and the most fundamental step for the automatic analysis of visual information. This paper introduces a quality adjustment framework to provide satisfactory object detection performance on wireless embedded cameras. Key components of the framework include a blind regression model for predicting the performance of object detection and two distortion type classifiers for determining the presence of noise and blur in an image. Experimental results show that the proposed framework achieves accurate estimations of image distortion types, and it can be easily applied on embedded cameras with low computational complexity to improve the quality of captured images.