We present an approach for identifying picturesque highlights from large amounts of egocentric video data. Given a set of egocentric videos captured over the course of a vacation, our method analyzes the videos and looks for images that have good picturesque and artistic properties. We introduce novel techniques to automatically determine aesthetic features such as composition, symmetry and color vibrancy in egocentric videos and rank the video frames based on their photographic qualities to generate highlights. Our approach also uses contextual information such as GPS, when available, to assess the relative importance of each geographic location where the vacation videos were shot. Furthermore, we specifically leverage the properties of egocentric videos to improve our highlight detection. We demonstrate results on a new egocentric vacation dataset which includes 26.5 hours of videos taken over a 14 day vacation that spans many famous tourist destinations and also provide results from a user-study to access our results.
In recent years, summarization efforts have started focussing on leveraging objects and activities within the scene. Features such as informative poses" @cite_33 and object of interest", based on labels provided by the user for a small number of frames @cite_44 , have helped in activity visualization, video summarization, and generating video synopsis from web-cam videos @cite_24 .
@cite_44 @cite_33 @cite_24
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Camera network systems generate large volumes of potentially useful data, but extracting value from multiple, related videos can be a daunting task for a human reviewer. Multicamera video summarization seeks to make this task more tractable by generating a reduced set of output summary videos that concisely capture important portions of the input set. We present a system that approaches summarization at the level of detected activity motifs and shortens the input videos by compacting the representation of individual activities. Additionally, redundancy is removed across camera views by omitting from the summary activity occurrences that can be predicted by other occurrences. The system also detects anomalous events within a unified framework and can highlight them in the summary. Our contributions are a method for selecting useful parts of an activity to present to a viewer using activity motifs and a novel framework to score the importance of activity occurrences and allow transfer of importance between temporally related activities without solving the correspondence problem. We provide summarization results for a two camera network, an eleven camera network, and data from PETS 2001. We also include results from Amazon Mechanical Turk human experiments to evaluate how our visualization decisions affect task performance. In video summarization, a short video clip is made from lengthy video without losing its semantic content using significant scenes containing important frames, called keyframes. This process finds importance in video content management systems. The proposed method involves automatic summarization of motion picture based on human face. In this method, those frames within which the appearances of an actor or actress, selected by the user, occurs are treated as keyframes. In the first step, the video is segmented into shots by Mutual Information. Then it detects the available faces in the frames of each shot using the local Successive Mean Quantization Transform (SMQT) features and Sparse Network of Winnows (SNoW) classifier. Then the face of an actor of interest is selected to match with different available faces, already extracted, using Eigenfaces method. A shot is taken into consideration, if the method succeeds in finding at least one matched face in the shot. The selected shots are finally combined to create summarized video. With the proliferation of wearable cameras, the number of videos of users documenting their personal lives using such devices is rapidly increasing. Since such videos may span hours, there is an important need for mechanisms that represent the information content in a compact form (i.e., shorter videos which are more easily browsable sharable). Motivated by these applications, this paper focuses on the problem of egocentric video summarization. Such videos are usually continuous with significant camera shake and other quality issues. Because of these reasons, there is growing consensus that direct application of standard video summarization tools to such data yields unsatisfactory performance. In this paper, we demonstrate that using gaze tracking information (such as fixation and saccade) significantly helps the summarization task. It allows meaningful comparison of different image frames and enables deriving personalized summaries (gaze provides a sense of the camera wearer's intent). We formulate a summarization model which captures common-sense properties of a good summary, and show that it can be solved as a submodular function maximization with partition matroid constraints, opening the door to a rich body of work from combinatorial optimization. We evaluate our approach on a new gaze-enabled egocentric video dataset (over 15 hours), which will be a valuable standalone resource. Given the enormous growth in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently. As these videos are generally of poor quality, summarization methods designed for well-produced videos do not generalize to them. To address this challenge, we propose to use web-images as a prior to facilitate summarization of user-generated videos. Our main intuition is that people tend to take pictures of objects to capture them in a maximally informative way. Such images could therefore be used as prior information to summarize videos containing a similar set of objects. In this work, we apply our novel insight to develop a summarization algorithm that uses the web-image based prior information in an unsupervised manner. Moreover, to automatically evaluate summarization algorithms on a large scale, we propose a framework that relies on multiple summaries obtained through crowdsourcing. We demonstrate the effectiveness of our evaluation framework by comparing its performance to that of multiple human evaluators. Finally, we present results for our framework tested on hundreds of user-generated videos.