A Unified Framework for Video Summarization, Browsing & by Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong

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By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang

Huge volumes of video content material can merely be simply accessed via speedy shopping and retrieval strategies. developing a video desk of contents (ToC) and video highlights to let finish clients to sift via all this knowledge and locate what they need, once they wish are crucial. This reference places forth a unified framework to combine those capabilities helping effective looking and retrieval of video content material. The authors have constructed a cohesive technique to create a video desk of contents, video highlights, and video indices that serve to streamline using purposes in purchaser and surveillance video functions. The authors speak about the new release of desk of contents, extraction of highlights, various thoughts for audio and video marker attractiveness, and indexing with low-level positive aspects similar to colour, texture, and form. present functions together with this summarization and skimming know-how also are reviewed. functions akin to occasion detection in elevator surveillance, spotlight extraction from activities video, and picture and video database administration are thought of in the proposed framework. This ebook offers the most recent in study and readers will locate their look for wisdom comfortable via the breadth of the knowledge coated during this quantity. * bargains the most recent in leading edge examine and functions in surveillance and buyer video* Presentation of a singular unified framework aimed toward effectively sifting in the course of the abundance of pictures accrued day-by-day at purchasing department stores, airports, and different advertisement amenities* Concisely written via top members within the sign processing with step by step guide in development video ToC and indices

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Additional resources for A Unified Framework for Video Summarization, Browsing & Retrieval: with Applications to Consumer and Surveillance Video

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Otherwise: (i) Create a new scene containing a single shot i and a single group j . (ii) Set numScenes = numScenes + 1. (5) Goto Step 2. 2). 2 An example video ToC. [findGroupSim] ● ● ● Input: Current shot and group structure. Output: Similarity between current shot and existing groups. Procedure: (1) Denote the current shot as shot i. (2) Calculate the similarities between shot i and existing groups: GroupSimi,g = ShotSimi,glast , g = 1, . . 22) where ShotSimi,j is the similarity between shots i and j ; and g is the index for groups and glast is the last (most recent) shot in group g.

2 Audio markers for sports highlights extraction. 3 Examples of visual markers for different sports. soccer, we want to detect the appearance of the goalpost. Correct detection of these key visual objects can eliminate the majority of the video content that is not in the vicinity of the interesting segments. For the goal of one general framework for all three sports, we use the following processing strategy: for the unknown sports content, we detect whether there are baseball catchers or golfers bending to hit the ball, or soccer goalposts.

By following the time flow, the viewer can browse through the video clip. 2 VIDEO BROWSING USING HIGHLIGHTS-BASED SUMMARY For representation based on play/break segmentation, browsing is also sequential, enabling a scan of all the play segments from the beginning of the video to the end. ” Note that “a golfer has a good hit” is represented by the detection of the golfer hitting the ball followed by the detection of applause from the audience. Similarly, that “there is a soccer goal attempt” is represented by the detection of the soccer goalpost followed by the detection of long and loud audience cheering.

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