Video Compression Estimating Recognition Accuracy for Remote Site Object Detection
Yusuke Shinohara, Hayato Itsumi, Florian Beye, Takanori Iwai · 2020
Current video compression algorithms are designed to achieve a smaller data size and enable higher human perception for the real time streaming of video applications. However, with the recent explosive technical progress of deep neural network (DNNs), video surveillance is increasingly being performed not by humans but by computer vision systems. In this work, we propose a video compression method for object detection by computer vision algorithms. Our method detects the ROI in an image and differentiates the image quality between the ROI and other areas. It also optimizes the image quality in the ROI by estimating the recognition accuracy of the object detection model. The results of an experimental evaluation demonstrate that our proposed method can achieve high-quality encoding in terms of data size and successfully estimate the recognition accuracy of the object detection model.