/pdf/the-scalable-video-coding-extension-of-the-h-264-avc-2pf4w9upmi.pdf

The Scalable Video Coding Extension of the H.264/AVC Standard

http://pancamr.lecture.ub.ac.id/files/2012/02/A-survey-on-vision-based-human-action-recognition_Image-and-Vision-Computing_2010.pdf

A survey on vision-based human action recognition

http://www.ens-lyon.fr/DI/wp-content/uploads/2009/07/LopesBetrouni.pdf

Fractal and multifractal analysis: a review.

This paper presents a survey of trajectory-based activity analysis for visual surveillance. It describes techniques that use trajectory data to define a general set of activities that are applicable to a wide range of scenes and environments. Events of interest are detected by building a generic topographical scene description from underlying motion structure as observed over time. The scene topology is automatically learned and is distinguished by points of interest and motion characterized by activity paths. The methods we review are intended for real-time surveillance through definition of a diverse set of events for further analysis triggering, including virtual fencing, speed profiling, behavior classification, anomaly detection, and object interaction.

http://cvrr.ucsd.edu/publications/2008/brendan_CSVT08.pdf

A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance

Current 3-D video (3DV) technology is based on stereo systems. These systems use stereo video coding for pictures delivered by two input cameras. Typically, such stereo systems only reproduce these two camera views at the receiver and stereoscopic displays for multiple viewers require wearing special 3-D glasses. On the other hand, emerging autostereoscopic multiview displays emit a large numbers of views to enable 3-D viewing for multiple users without requiring 3-D glasses. For representing a large number of views, a multiview extension of stereo video coding is used, typically requiring a bit rate that is proportional to the number of views. However, since the quality improvement of multiview displays will be governed by an increase of emitted views, a format is needed that allows the generation of arbitrary numbers of views with the transmission bit rate being constant. Such a format is the combination of video signals and associated depth maps. The depth maps provide disparities associated with every sample of the video signal that can be used to render arbitrary numbers of additional views via view synthesis. This paper describes efficient coding methods for video and depth data. For the generation of views, synthesis methods are presented, which mitigate errors from depth estimation and coding.

3-D Video Representation Using Depth Maps

Event analysis in videos is a critical task in many applications. Activity recognition that aims to recognize actions from video and in particular abnormal event recognition in surveillance video has received significant attention from the research community. In this special issue, we focus on event analysis in broad problem domains. Event recognition in specific domains, such as highlight detection in sports videos, has attracted much interest in the past decade. Recently, due to the emergence of online video search, the research community has become interested in event content analysis for both broadcast and user-generated videos. For news videos, Large-Scale Concept Ontology for Multimedia (LSCOM) has defined 56 event/activity concepts, covering a broad range of events such as airplane flying, car crash, riot, people marching, and so on. Researchers have also started to investigate event recognition from other video sources, such as education videos and medical videos. For these applications, we have witnessed the effectiveness of using both static and temporal information.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY Editor-in-Chief

Editorial Office Journal of Experimental Biology The Company of Biologists Limited Bidder Building Station Road Histon Cambridge CB24 9LF, UK Tel: +44 1223 632871 Fax: +44 1223 632873 jeb@biologists.com Editorial Advisory Board Stephen C. Adolph (Harvey Mudd College, USA) Philip E. Bickler (University of California San Francisco, USA) Verner P. Bingman (Bowling Green State University, USA) Simon Blomberg (University of Queensland, Australia) Elizabeth L. Brainerd (Brown University, USA) C.-H. Christina Cheng (University of Illinois UrbanaChampaign, USA) Geoffrey M. Coast (Birkbeck, University of London, UK) Thomas S. Collett (University of Sussex, UK) Andrew R. Cossins (University of Liverpool, UK) Mark W. Denny (Stanford University, USA) Heather Eisthen (Michigan State University, USA) Walter Federle (University of Cambridge, UK) John M. Gosline (University of British Columbia, Canada) Jan Hemmi (University of Western Australia, Australia) David Hu (Georgia Institute of Technology, USA) Sönke Johnsen (Duke University, USA) Kirk C. Klasing (University of California Davis, USA) Michael F. Land (University of Sussex, UK) Stan L. Lindstedt (Northern Arizona University, USA) William K. Milsom (University of British Columbia, Canada) Rajat Mittal (Johns Hopkins University, USA) Cynthia F. Moss (University of Maryland, USA) Yoshitaka Nagahama (National Institute for Basic Biology, Japan) Gabrielle A. Nevitt (University of California Davis, USA) Göran E. Nilsson (University of Oslo, Norway) Michael J. O’Donnell (McMaster University, Canada) Kenneth R. Olson (Indiana University, USA) Frank Seebacher (University of Sydney, Australia) Kenneth Söderhäll (Uppsala University, Sweden) George N. Somero (Stanford University, USA) Eric D. Tytell (Tufts University, USA) Tobias Wang (University of Aarhus, Denmark) Z. Jane Wang (Cornell University, USA) Craig R. White (Monash University, Australia) Alan M. Wilson (The Royal Veterinary College, UK) Christopher M. Wood (McMaster University, Canada) Patricia A. Wright (University of Guelph, Canada)

Deputy Editor-in-Chief

In this paper, we propose to build a novel benchmark and neural video coding task named learning based Unmanned Aerial Vehicle (UAV) video coding. We collect the UAV videos with different content variations, including in-door and out-door scenes, object-scale variations and viewpoint distance, different climate condition etc. Then we encode those properly-selected videos using popular end-to-end optimized video codecs and conventional hybrid codecs, to form a comprehensive benchmark for learned drone video compression. We also provide a detailed analysis and envision the challenge of such task for future research. The main contributions of this paper are three folds. First, we construct a comprehensive benchmark for the task of drone video compression which consists of the rate-distortion (R-D) behavior of both hybrid and learned video codecs. To our knowledge, it is the first attempt in end-to-end optimized solution to compress drone videos. Second, we provide the review and analysis of the learned drone video compression schemes and further discuss the challenges of encoding UAV videos. Third, this benchmark and related research is accomplished as a milestone MPAI End-to-end Video (EEV) coding project. The proposed benchmark has constructed a solid baseline for compressing UAV videos and facilitates the future research works for related task.

Wen Gao

Papers

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY Editor-in-Chief

Deputy Editor-in-Chief

Learning to Compress Unmanned Aerial Vehicle (UAV) Captured Video: Benchmark and Analysis

Semantic-Aware Visual Decomposition for Image Coding