The interpretation of video imagery is the quintessential goal of computer vision. The ability to group moving pixels into regions and then associate those regions with semantic labels has long been studied by the vision community. In urban nighttime scenarios, the difficulty of this task is simultaneously alleviated and compounded. At night there is typically less movement in the scene, which makes the detection of relevant motion easier. However, the poor quality of the imagery makes it more difficult to interpret actions from these motions. In this paper, we present a system capable of detecting moving objects in outdoor nighttime video. We focus on visible-and-near-infrared (VNIR) cameras, since they offer low cost and very high resolution compared to alternatives such as thermal infrared. We present empirical results demonstrating system performance on a parking lot surveillance scenario. We also compare our results to a thermal infrared sensor viewing the same scene.
展开▼
