By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang
Huge volumes of video content material can purely be simply accessed via quick shopping and retrieval ideas. developing a video desk of contents (ToC) and video highlights to let finish clients to sift via all this knowledge and locate what they wish, after they wish are crucial. This reference places forth a unified framework to combine those capabilities aiding effective looking and retrieval of video content material. The authors have built a cohesive method to create a video desk of contents, video highlights, and video indices that serve to streamline using purposes in patron and surveillance video functions. The authors speak about the iteration of desk of contents, extraction of highlights, assorted innovations for audio and video marker acceptance, and indexing with low-level positive factors similar to colour, texture, and form. present purposes together with this summarization and perusing expertise also are reviewed. functions similar 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 learn and readers will locate their look for wisdom glad by way of the breadth of the knowledge lined during this quantity. * deals the most recent in innovative study and functions in surveillance and patron video* Presentation of a unique unified framework aimed toward effectively sifting throughout the abundance of pictures accumulated day-by-day at buying department stores, airports, and different advertisement amenities* Concisely written through prime individuals within the sign processing with step by step guide in development video ToC and indices
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Additional info for A Unified Framework for Video Summarization, Browsing & Retrieval: with Applications to Consumer and Surveillance Video
This approach works well when the number of key frames is not large. However, this approach does not scale to long video clips. 1. 1), related shots are merged into groups, based on which a browsing tree can be constructed [3, 5]. Zhang et al.  divide the whole video stream into multiple video segments, each of which contains an equal number of consecutive shots. Each segment is further divided into subsegments, thus constructing a hierarchy of video content that is used to assist browsing. In this approach, time is the only factor considered, and no visual content is used in constructing the browsing hierarchy.
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. The detection results can enable us to decide which sport (baseball, golf, or soccer) is being played.
24) merge the scene that group g belongs to into the scene that group gmax belongs to. That is, if a scene contains a shot that is interlaced with the current scene, merge the two scenes. 3 (shot i = shot 4, gmax = 0, g = 1, top = shot 1, and bottom = shot 4). (4) Return. [updateScene] ● ● Input: Current shot, group structure, and scene structure. Output: An updated version of scene structure. 3 The Proposed Approach before top 29 after bottom shot 0 1 2 3 4 group 0 0 1 1 scene 0 0 1 1 . . update 0 1 2 3 4 0 0 0 1 1 0 0 0 0 0 0 0 .
A Unified Framework for Video Summarization, Browsing & Retrieval: with Applications to Consumer and Surveillance Video by Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang