Adaptive denoising filtering for object detection applications

The widespread of augmented reality applications, cognitive video surveillance, autonomous or supportive navigation systems, has increased the importance of accurate object detection algorithms. However, the presence of noise depending on the characteristics of the acquisition device, on lighting intensity and directions, and on weather conditions, could severely degrade the performance of such applications. As a matter of fact, effective ad-hoc denoising strategies are required since traditional noise removal algorithms designed to improve the quality of the image, could even worsen the accuracy of detection. This paper presents a low-cost adaptive filtering strategy that adapts the characteristics of the filter depending on the impact of each image region on the feature sets. This solution permits improving the correct detection percentage of approximately 30%with respect to using noisy images. The approach is generally intended for object detection algorithms based on Histogram-of-Oriented-Gradients (HOG) and can run in real time on a limited complexity hardware.