The optimal use of batteries in drones is a critical issue for achieving both reliable operation and maximum flight time. The key is to acquire accurate information about the state of charge (SoC) of the battery in runtime. Drones typically employ series-connected lithium-ion polymer (Li-Po) battery cells, whose SoC is affected by many environmental factors as well as flight patterns. In this article, we propose a scheme, called Optrone, which maximizes the flight time of a drone while safely using the battery. Understanding the implications of the factors affecting the SoC of the drone's battery pack, we propose a three-level SoC, which is a metric for representing the SoC of a battery in runtime. We also provide various operating policies to users to improve the safety and efficiency of operating the drone. We implemented the prototype hardware and software for Optrone, and validated its operation in controlled and real environments. The experimental results in a controlled environment showed that the proposed three-level SoC poses less than 3% error and the operating policies achieved a flight time gain of 19.4%, while guaranteeing battery safety. We also observed a flight time gain of about 10% in real outdoor experiments, where the user rightly adheres to the advised Optrone policy.

We address the problem of highlight detection from a 360◦ video by summarizing it both spatially and temporally. Given a long 360◦ video, we spatially select pleasantly-looking normal field-of-view (NFOV) segments from unlimited field of views (FOV) of the 360◦ video, and temporally summarize it into a concise and informative highlight as a selected subset of subshots. We propose a novel deep ranking model named as Composition View Score (CVS) model, which produces a spherical score map of composition per video segment, and determines which view is suitable for highlight via a sliding window kernel at inference. To evaluate the proposed framework, we perform experiments on the Pano2Vid benchmark dataset (Su, Jayaraman, and Grauman 2016) and our newly collected 360◦ video highlight dataset from YouTube and Vimeo. Through evaluation using both quantitative summarization metrics and user studies via Amazon Mechanical Turk, we demonstrate that our approach outperforms several state-of-the-art highlight detection methods.We also show that our model is 16 times faster at inference than AutoCam (Su, Jayaraman, and Grauman 2016), which is one of the first summarization algorithms of 360◦ videos.