Understanding the Impact of Compression on Feature Detection and Matching in Computer Vision

As video-based sensor networks continue to scale and become more ubiquitous, it is becoming increasingly important to focus systems research on techniques that support content-based decisions in real-time towards the edge of the network. While some prior work has focused on high-level image and video quality's effect on computer vision (e.g., object recognition). We are unaware of any work that focuses on the low-level details of why. This paper explores the impact of compression on underlying computer vision techniques. Specifically, this paper focuses on understanding the fundamental impact of compression on SIFT feature detection and matching. We show how reduced resolution or frame quality can negatively impact feature detection and tracking.