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

Huge volumes of video content material can purely be simply accessed by way of swift shopping and retrieval suggestions. developing a video desk of contents (ToC) and video highlights to let finish clients to sift via all this information and locate what they need, once they wish are crucial. This reference places forth a unified framework to combine those services helping effective searching and retrieval of video content material. The authors have constructed a cohesive strategy to create a video desk of contents, video highlights, and video indices that serve to streamline using functions in customer and surveillance video functions.

The authors talk about the new release of desk of contents, extraction of highlights, diversified concepts for audio and video marker acceptance, and indexing with low-level positive aspects comparable to colour, texture, and form. present purposes together with this summarization and skimming know-how also are reviewed. purposes reminiscent of occasion detection in elevator surveillance, spotlight extraction from activities video, and photo and video database administration are thought of in the proposed framework. This e-book offers the newest in study and readers will locate their look for wisdom completely happy by means of the breadth of the knowledge lined during this quantity.

* deals the most recent in leading edge study and purposes in surveillance and buyer video

* Presentation of a unique unified framework aimed toward effectively sifting in the course of the abundance of pictures accumulated day-by-day at buying department shops, airports, and different advertisement facilities

* Concisely written by means of prime participants within the sign processing with step by step guide in development video ToC and indices

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Extra info for A Unified Framework for Video Summarization, Browsing and Retrieval. With Applications to Consumer and Surveillance Video

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18) scene2 groups: shot (19, 20, 22, 24, 26, 27, 30) group6: shot (21, 28, 29. 31, 32, 34) group7: shot (23. 25. 33. 35. 37) group8: shot (36. 2 group9:shot(39. 40. 41) 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 /. 22) where ShotSimtj is the similarity between shots / and j \ and g is the index for groups and giast is the last (most recent) shot in group g.

18) scene2 groups: shot (19, 20, 22, 24, 26, 27, 30) group6: shot (21, 28, 29. 31, 32, 34) group7: shot (23. 25. 33. 35. 37) group8: shot (36. 2 group9:shot(39. 40. 41) 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 /. 22) where ShotSimtj is the similarity between shots / and j \ and g is the index for groups and giast is the last (most recent) shot in group g.

9. The cameras are usually positioned in the center of the two sides of the field. The camera operators pan the camera in order to go back and forth between two halves of the field and zoom to focus on special targets. Since the distance between the camera and either of the two goalposts is relatively much larger than the size of the goalpost itself, little change occurs in the pose of the goalpost during the entire game, irrespective of the camera pan or zoom. 9. 11. Robust identification of those video frames containing either of the two goalposts can bring us to the vicinity of soccer highlights.

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