By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang
Huge volumes of video content material can basically be simply accessed by means of quick looking and retrieval ideas. developing a video desk of contents (ToC) and video highlights to permit finish clients to sift via all this knowledge and locate what they wish, once they wish are crucial. This reference places forth a unified framework to combine those capabilities helping effective searching and retrieval of video content material. The authors have built a cohesive solution to create a video desk of contents, video highlights, and video indices that serve to streamline using functions in buyer and surveillance video functions. The authors speak about the iteration of desk of contents, extraction of highlights, various ideas for audio and video marker attractiveness, and indexing with low-level positive factors reminiscent of colour, texture, and form. present purposes together with this summarization and skimming expertise also are reviewed. functions akin to 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 provides the newest in learn and readers will locate their look for wisdom pleased via the breadth of the knowledge coated during this quantity. * deals the most recent in leading edge examine and functions in surveillance and shopper video* Presentation of a singular unified framework aimed toward effectively sifting throughout the abundance of pictures accumulated day-by-day at purchasing department shops, airports, and different advertisement amenities* Concisely written via major individuals 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 & Retrieval: with Applications to Consumer and Surveillance Video
The video ToC is constructed as a sequence of the key frames. A user can access the video by browsing through the sequence of key frames. The supporting techniques of this approach, automatic shot boundary detection and key frame extraction, are summarized as follows: ● Shot boundary detection. In general, automatic shot boundary detection techniques can be classiﬁed into ﬁve categories: pixel based, statistics based, transform based, feature based, and histogram based. Pixel-based approaches use the pixel-wise intensity difference as the indicator for shot boundaries [1, 7].
2 VISUAL MARKER DETECTION As deﬁned earlier, visual markers are key visual objects that indicate the interesting segments. 3 shows examples of some visual markers for three different games. For baseball games, we want to detect the pattern in which the catcher squats waiting for the pitcher to pitch the ball; for golf games, we want to detect the players bending to hit the golf ball; and for 39 40 3. Highlights Extraction from Unscripted Video Audio Video Audio Marker Detection Applause Visual Marker Detection Cheers Soccer Goalpost Excited Speech Baseball Catcher Golfer Bending to Hit A-V Marker Association Which sport is it?
Speciﬁcally, WC , and WA are determined automatically by the algorithm, and groupThreshold and sceneThreshold are determined by user’s interaction. 19), we combine color histogram similarity and activity similarity to form the overall shot similarity. Since the color histogram feature and activity feature are from two totally different physical domains, it would be meaningless to combine them without normalizing them ﬁrst. The Gaussian normalization process ensures that entities from different domains are normalized to the same dynamic range.