The emergence of low-cost personal mobiles devices and wearable cameras and the increasing storage capacity of video-sharing websites have pushed forward a growing interest towards first-person videos. Since most of the recorded videos compose long-running streams with unedited content, they are tedious and unpleasant to watch. The fast-forward state-of-the-art methods are facing challenges of balancing the smoothness of the video and the emphasis in the relevant frames given a speed-up rate. In this work, we present a methodology capable of summarizing and stabilizing egocentric videos by extracting the semantic information from the frames. This paper also describes a dataset collection with several semantically labeled videos and introduces a new smoothness evaluation metric for egocentric videos that is used to test our method.
More recent methods have adopted the optimization of frame selection @cite_3 , @cite_13 , @cite_17 . @cite_13 focus on an adaptive frame selection based on minimizing an energy function. They modeled the video as a graph by mapping the frames as the nodes and the edges weight reflecting the cost of the transition between the frames in the final video. The shortest path in the graph produces the best frames transitions for the final video composition. In the work of @cite_3 they present a more sophisticated algorithm which optimally selects frames from the input video as result of a joint optimization of camera motion smoothing and speed-up. They also perform a 2D video stabilization to create the hyperlapse result. @cite_17 extended the work of expanding the field of view of the output video by using a mosaicking approach on the input frames with single or multiple egocentric videos.
@cite_13 @cite_3 @cite_17
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While egocentric cameras like GoPro are gaining popularity, the videos they capture are long, boring, and difficult to watch from start to end. Fast forwarding (i.e. frame sampling) is a natural choice for faster video browsing. However, this accentuates the shake caused by natural head motion, making the fast forwarded video useless. Long videos can be played much faster than real-time by recording only one frame per second or by dropping all but one frame each second, i.e., by creating a timelapse. Unstable hand-held moving videos can be stabilized with a number of recently described methods. Unfortunately, creating a stabilized timelapse, or hyperlapse, cannot be achieved through a simple combination of these two methods. Two hyperlapse methods have been previously demonstrated: one with high computational complexity and one requiring special sensors. We present an algorithm for creating hyperlapse videos that can handle significant high-frequency camera motion and runs in real-time on HD video. Our approach does not require sensor data, thus can be run on videos captured on any camera. We optimally select frames from the input video that best match a desired target speed-up while also resulting in the smoothest possible camera motion. We evaluate our approach using several input videos from a range of cameras and compare these results to existing methods. The possibility of sharing one's point of view makes use of wearable cameras compelling. These videos are often long, boring and coupled with extreme shake as the camera is worn on a moving person. Fast forwarding (i.e. frame sampling) is a natural choice for faster video browsing. However, this accentuates the shake caused by natural head motion in an egocentric video, making the fast forwarded video useless. We propose EgoSampling, an adaptive frame sampling that gives more stable, fast forwarded, hyperlapse videos. Adaptive frame sampling is formulated as energy minimization, whose optimal solution can be found in polynomial time. We further turn the camera shake from a drawback into a feature, enabling the increase of the field-of-view. This is obtained when each output frame is mosaiced from several input frames. Stitching multiple frames also enables the generation of a single hyperlapse video from multiple egocentric videos, allowing even faster video consumption.