IEEE Transactions on Circuits and Systems for Video Technology information for authors

  title={IEEE Transactions on Circuits and Systems for Video Technology information for authors},
  author={Chang Wen Chen and Hamid Gharavi and Thomas Sikora and Ishfaq Ahmad and John F. Arnold and Oscar C. Au and Mauro Barni and Vincent Bottreau and Jill M. Boyce and Thomson Corp and Jianfei Cai and Homer H. Chen and Shao-Yi Chien and Mary L. Comer and Paulo Lobato Correia and Ricardo De Queiroz and Pascal Frossard and Toshiaki Fujii and Wen Gao and R. J. Green and Yo-Sung Ho and Ebroul Izquierdo and Queen Mary and Rosa Lancini and Shipeng Li and Xin Li and Xuelong Li and Ant{\'o}nio Navarro and Sharath Pankanti and Justin Ridge and Yong Rui and Dan Schonfeld and Eckehard G. Steinbach and Sanghoon Sull and Huifang Sun and Clark N. Taylor and Deepak S. Turaga and Gene Wen and Thomas Wiegand and Dapeng Oliver Wu and Jar Ferr Yang and Haoping Yu},
  journal={IEEE Transactions on Circuits and Systems for Video Technology},
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… 

Video event detection framework on large-scale video data

This work considers video data as a sequence of images that forms a 3-dimensional spatiotemporal structure, and performs multiview orthographic projection to transform the video data into 2-dimensional representations, allowing a unique way to represent video events and capture the temporal aspect of video data.

An Implementation of Moving Object Detection in Compressed Domain of HEVC for Video Surveillance with Results

An approach to detect the moving object in compressed domain HEVC is discussed which is used in video surveillance and implementation and experimenta l results are presented.

Moving Object Detection in Compressed Domain of HEVC for Video Surveillance

An approach to detect the moving object in compressed domain HEVC is discussed, which is used in video surveillance and makes use of motion vector, cod ing unit and prediction unit of HEVC.

Computer vision aided video coding

This chapter presents dynamic background modeling techniques to generate a dynamic frame popularly known as McFIS from dynamically challenging environments for 2 detecting moving objects, and a number of advanced video coding techniques for improving rate-distortion performance as well as reducing computational complexity compared to the state-of-the-arts methods.

Survey Paper on HEVC Standard on Moving Object Detection in Compressed Domain to Analyze the Input Frame Verses Output Frame

A real-time HEVC compressed domain moving object segmentation algorithm for surveillance videos is proposed in this paper because High Efficiency Video Coding (HEVC) has a large potential to identify events by reusing coding structures in HEVC, which saves vast extent of computational resources.

Robust candidate frame detection in videos using semantic content modeling

  • T. ManonmaniK. Mala
  • Computer Science
    2014 International Conference on Communication and Network Technologies
  • 2014
Speeded Up Robust Features (SURF) are used to detect the candidate frames among the set of key frames extracted from a video content and eliminate the duplicate frames without a prior knowledge of the video content.

Video-zilla: An Indexing Layer for Large-Scale Video Analytics

This paper proposes a video data unit abstraction, semantic video stream (SVS), based on a notion of distance between objects in the video, and builds a hierarchical index that exposes the semantic similarity both within and across camera feeds, such that Video-zilla can quickly cluster video feeds based on their content semantics without manual labeling.

International Advanced Research in Computer Science and Software Engineering Latest Video Compression Standard H.264 Within Video Surveillance

The availability of cheap, high-performance processors together with the development of international standards for video compression has enabled a wide range of video communications applications.

A Novel Hierarchical Dynamic Video Summarization Representation for Video Analysis

Based on Rough Sets (RS), a novel effective video summarization representation was proposed for video analysis in compressed domain and becomes more scientific and efficient than previous methods.

An Algorithm for Shot Boundary Detection and Key Frame Extraction Using Histogram Difference

With the square histogram difference considered at block level for the video frames, a new method of extracting the keyframes based on shot type is presented.



Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates

  • Jaechul KimK. Grauman
  • Computer Science
    2009 IEEE Conference on Computer Vision and Pattern Recognition
  • 2009
Experimental results on surveillance videos show that the space-time MRF model robustly detects abnormal activities both in a local and global sense: not only does it accurately localize the atomic abnormal activities in a crowded video, but at the same time it captures the global-level abnormalities caused by irregular interactions between local activities.

Recognizing human group activities with localized causalities

The aim of this paper is to address the problem of recognizing human group activities in surveillance videos by proposing to encode the group-activities with three types of localized causalities, namely self-causality, pair-causeality, and group-causes, which characterize the local interaction/reasoning relations within, between, and among motion trajectories of different humans respectively.

Tracking and Activity Recognition Through Consensus in Distributed Camera Networks

This paper investigates distributed scene analysis algorithms by leveraging upon concepts of consensus that have been studied in the context of multiagent systems, but have had little applications in video analysis.

Online learning of robust object detectors during unstable tracking

This work proposes a new approach, called Tracking-Modeling-Detection (TMD), that closely integrates adaptive tracking with online learning of the object-specific detector and shows the real-time learning and classification is achievable with random forests.

Active learning for on-road vehicle detection: a comparative study

This study provides a cost-sensitive analysis of three popular active learning methods for on-road vehicle detection through learning experiments performed with detectors based on histogram of oriented gradient features and SVM classification (HOG–SVM), and Haar-like features and Adaboost classification (Haar–Adaboost).

Real-time human detection using contour cues

A real-time and accurate human detector, C4, is proposed in this paper. C4 achieves 20 fps speed and state-of-the-art detection accuracy, using only one processing thread without resorting to special

Quantized wavelet features and support vector machines for on-road vehicle detection

This work argues and demonstrates experimentally that the actual coefficient values are less important compared to the simple presence or absence of those coefficients, and proposes quantizing large negative coefficients to -1, large positive coefficients to 1, and setting the rest coefficients to 0.

Group Activity Recognition by Gaussian Processes Estimation

Gaussian Processes are introduced to represent human motion trajectories from a probabilistic perspective to handle the variability of people’s activities in group to solve the problem of unbalanced number of persons in different activities.

Robust Real-time Object Detection

A visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described, with the introduction of a new image representation called the “Integral Image” which allows the features used by the detector to be computed very quickly.

Abnormal detection using interaction energy potentials

A new method to detect abnormal behaviors in human group activities based on social behavior analysis, which uses an interaction energy potential function to represent the current behavior state of a subject, and velocity is used as its actions.