Influence of Image/Video Compression on Night Vision Based Pedestrian Detection in an Automotive Application
With the introduction of IP/Ethernet networks in automobiles, video compression becomes mandatory for efficient video transfer. Lossy image and video compression standards, such as JPEG and H.264/AVC, achieve high compression efficiency, but also introduce irreversible modification to the video data. On the one hand, this can lead to perceivable image degradation, and on the other hand, affect the performance of image processing algorithms. The pedestrian detection application based on night vision technology, is one example of a driver assistance service using a video stream as input. In this paper, we examine the impact of image/video compression on pedestrian detection, which employs a far infrared (FIR) sensor. In order to measure application quality, metrics used for driver assistance system validation, are investigated through hardware in the loop (HIL) simulation.