In this paper we propose a system for the analysis of user generated video (UGV). UGV often has a rich camera motion structure that is generated at the time the video is recorded by the person taking the video, i.e., the ¿camera person.¿ We exploit this structure by defining a new concept known ascamera viewfor temporal segmentation of UGV. The segmentation provides a video summary with unique properties that is useful in applications such as video annotation. Camera motion is also a powerful feature for identification of keyframes and regions of interest (ROIs) since it is an indicator of the camera person's interests in the scene and can also attract the viewers' attention. We propose a new location-based saliency map which is generated based on camera motion parameters. This map is combined with other saliency maps generated using features such as color contrast, object motion and face detection to determine the ROIs. In order to evaluate our methods we conducted several user studies. A subjective evaluation indicated that our system produces results that is consistent with viewers' preferences. We also examined the effect of camera motion on human visual attention through an eye tracking experiment. The results showed a high dependency between the distribution of fixation points of the viewers and the direction of camera movement which is consistent with our location-based saliency map.

Recent widespread use of multimedia-enabled devices has prompted the industry to look at systems which can provide a distributed, synchronized content consumption experience. Motorola has partnered with Purdue University's eStadium project to create the Rich Immersive Sports Experience (RISE) project. RISE is an attempt to design and develop such a system geared toward use in suites in Purdue's Ross-Ade football stadium. The core of the system is Motorola's Media Bundle technology that represents content and timing information for the TV screens and smart phones used in the system. This paper discusses the design process for the RISE system, how Media Bundles are used, and our solutions to issues of synchronization, speed, reliability, and scalability.

Compact representations of video data greatly enhances efficient video browsing. Such representations provide the user with information about the content of the particular sequence being examined while preserving the essential message. We propose a method to automatically generate video summaries using transcripts obtained by automatic speech recognition. We divide the full program into segments based on pause detection and derive a score for each segment, based on the frequencies of the words and bigrams it contains. Then, a summary is generated by selecting the segments with the highest score to duration ratios while at the same time maximizing the coverage of the summary over the full program. We developed an experimental design and a user study to judge the quality of the generated video summaries. We compared the informativeness of the proposed algorithm with two other algorithms for three different programs. The results of the user study demonstrate that the proposed algorithm produces more informative summaries than the other two algorithms

This paper describes a speaker detection system using cross-modal association methods. Four association approaches are designed using linear and nonlinear association models. Speaker detection experiments were conducted to compare the approaches

In this paper, we describe an approach that uses a combination of visual and audio features to cluster shots belonging to the same person in video programs. We use color histograms extracted from keyframes and faces, as well as cepstral coefficients derived from audio to calculate pairwise shot distances. These distances are then normalized and combined to a single confidence value which reflects our certainty that two shots contain the same person. We then use an agglomerative clustering algorithm to cluster shots based on these confidence values. We report the results of our system on a data set of approximately 8 hours of programming.

In this paper, we describe a unique new paradigm for video database management known as ViBE (video indexing and browsing environment). ViBE is a browseable/searchable paradigm for organizing video data containing a large number of sequences. The system first segments video sequences into shots by using a new feature vector known as the Generalized Trace obtained from the DC-sequence of the compressed data. Each video shot is then represented by a hierarchical structure known as the shot tree. The shots are then classified into pseudo-semantic classes that describe the shot content. Finally, the results are presented to the user in an active browsing environment using a similarity pyramid data structure. The similarity pyramid allows the user to view the video database at various levels of detail. The user can also define semantic classes and reorganize the browsing environment based on relevance feedback. We describe how ViBE performs on a database of MPEG sequences.

We propose an algorithm to detect scene changes in a video sequence in the compressed domain. We define a feature vector extracted from each frame that we call the generalized trace. We examine various ways of processing the generalized trace to determine the temporal location of scene changes in a video stream.