By Ziyou Xiong, Regunathan Radhakrishnan, Ajay Divakaran, Yong Rui, Thomas S. Huang
Huge volumes of video content material can in basic terms be simply accessed via quick shopping and retrieval thoughts. developing a video desk of contents (ToC) and video highlights to let finish clients to sift via all this information and locate what they wish, once they wish are crucial. This reference places forth a unified framework to combine those capabilities aiding effective shopping and retrieval of video content material. The authors have built a cohesive technique to create a video desk of contents, video highlights, and video indices that serve to streamline using functions in client and surveillance video purposes. The authors talk about the iteration of desk of contents, extraction of highlights, assorted strategies for audio and video marker acceptance, and indexing with low-level positive factors akin to colour, texture, and form. present purposes together with this summarization and skimming expertise also are reviewed. purposes corresponding 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 offers the newest in study and readers will locate their look for wisdom pleased through the breadth of the knowledge coated during this quantity. * bargains the most recent in innovative study and purposes in surveillance and customer video* Presentation of a unique unified framework geared toward effectively sifting during the abundance of photos amassed day-by-day at purchasing department shops, airports, and different advertisement amenities* Concisely written by way of best 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
6) where x and y are two arbitrary frames, with x > y. 11) where avgShotLength is the average shot length of the whole video stream; MULTIPLE is a constant that controls how fast the temporal attraction will decrease to 0. For our experiment data, we ﬁnd MULTIPLE = 10 gives good results. The preceding deﬁnition of temporal attraction says that the farther apart the frames, the less the temporal attraction. If the frame difference is larger than MULTIPLE times the average shot length, the attraction decreases to 0.
We disregard the V component because it is less robust to the lighting condition. At the key frame level, visual features are extracted to characterize the spatial information. 4) where bi and ei are the beginning and ending frames of shot i. 5) which captures both the spatial and the temporal information of a shot. At higher levels, this spatial-temporal information is used in grouping and scene structure construction. 3 TIME-ADAPTIVE GROUPING Before we construct the scene structure, it is convenient to ﬁrst create an intermediate entity group to facilitate the process.
6. 2 Related Work Work on extracting video ToC has been done at various levels (key frame, shot, group, and scene). Next, we brieﬂy review and evaluate some of the common approaches proposed. 1 SHOT- AND KEY FRAME–BASED VIDEO TOC In this approach, the raw video stream is ﬁrst segmented into a sequence of shots by using automatic shot boundary detection techniques. Key frames are then extracted from the segmented shots. 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.