Localization for Anchoritic Sensor Networks

We introduce a class of anchoritic sensor networks, where communications between sensor nodes is undesirable or infeasible, e.g., due to harsh environment, energy constraints, or security considerations

Rain Removal in Traffic Surveillance: Does it Matter?

Varying weather conditions, including rainfall and snowfall, are generally regarded as a challenge for computer vision algorithms. One proposed solution to the challenges induced by rain and snowfall is to artificially remove the rain from images or video using rain removal algorithms. It is the promise of these algorithms that the rain-removed image frames will improve the performance of subsequent segmentation and tracking algorithms. However, rain removal algorithms are typically evaluated on their ability to remove synthetic rain on a small subset of images. Currently, their behavior is unknown on real-world videos when integrated with a typical computer vision pipeline. In this paper, we review the existing rain removal algorithms and propose a new dataset that consists of 22 traffic surveillance sequences under a broad variety of weather conditions that all include either rain or snowfall. We propose a new evaluation protocol that evaluates the rain removal algorithms on their ability to improve the performance of subsequent segmentation, instance segmentation, and feature tracking algorithms under rain and snow. If successful, the de-rained frames of a rain removal algorithm should improve segmentation performance and increase the number of accurately tracked features. The results show that a recent single-frame-based rain removal algorithm increases the segmentation performance by 19.7% on our proposed dataset, but it eventually decreases the feature tracking performance and showed mixed results with recent instance segmentation methods. However, the best video-based rain removal algorithm improves the feature tracking accuracy by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System

The Monkey, the Ant, and the Elephant:Addressing Safety in Smart Spaces

Smart spaces deliver digital services to optimize space use and enhance user experience. The impact of ill-programmed applications in such spaces goes beyond loss of data or a computer crash; there is the potential risk of physical harm to the space and its users. Ensuring safety in this type of cyberphysical system is critically important