【Pattern Recognition】 Special Issue: Video Analytics with Deep Learning
http://www.journals.elsevier.com/pattern-recognition/call-for-papers/pattern-recognition-special-issue-video-analytics-with-deep/
Pattern Recognition Special Issue: Video Analytics with Deep Learning
We are living in a world where we are surrounded by so many intelligent video-capturing devices.These devices capture data about how we live and what we do.For example, thanks to surveillance and action cameras, as well as smart phones and even old-fashioned camcorders, we are able to record videos at an unprecedented scale and pace. There is exceedingly rich information and knowledge embedded in all those videos. With the recent advances in computer vision, we now have the ability to mine such massive visual data to obtain valuable insight about what is happening in the world.Due to the remarkable successes of deep learning techniques, we are now able to boost video analysis performance significantly and initiate new research directions to analyze video content. For example, convolutional neural networks have demonstrated superiority on modeling high-level visual concepts, while recurrent neural networks have shown promise in modeling temporal dynamics in videos. Deep video analytics, or video analytics with deep learning, is becoming an emerging research area in the field of pattern recognition.
The goal of this special issue is to call for a coordinated effort to understand the opportunities and challenges emerging in video analysis with deep learning techniques, identify key tasks and evaluate the state of the art, showcase innovative methodologies and ideas, introduce large scale real systems or applications, as well as propose new real-world datasets and discuss future directions. The video data of interest cover a wide spectrum, ranging from first-person wearable videos, web videos (aka user-generated content), commercial video programs, to surveillance videos. Video analytics plays an important role in public security, entertainment, healthcare, and so on. We solicit manuscripts in all fields of video analytics that explore the synergy of video understanding and deep learning techniques.
We believe the special issue will offer a timely collection of research updates to benefit the researchers and practitioners working in the broad computer vision and pattern recognition communities. To this end, we solicit original research and survey papers addressing the topics listed below (but not limited to):
Topics:
[*]First-person/wearable video analysis using deep learning techniques, including object detection and recognition, highlight detection, action recognition, event detection, segmentation and tracking, classification, summarization and storytelling, editing, data collection and benchmarking, and so on.
[*]Video and language – describing video with natural language using deep learning techniques.
[*]Web video understanding using deep learning techniques, including classification, annotation, event detection and recognition, authoring and editing, and summarization.
[*]Home/public video surveillance using deep learning, including motion detection and classification, scene understanding, event detection and recognition, people analysis, object tracking and segmentation, human computer/robot interaction, behavior recognition, crowd analysis, fusion of vision with other sensing modalities, and so on.
[*]Data collections, benchmarking, and performance evaluation.
Submission Guideline:Manuscripts should be formatted and submitted online according to the instructions for Pattern Recognition athttp://www.elsevier.com/journals/pattern-recognition/0031-3203/guide-for-authors. The authors must select “SI:Video Anal Deep Learning” when specifying the “Article Type” in the submission system. Submitted manuscripts must not have appeared or been under review elsewhere. All submitted manuscripts will be reviewed by at least three reviewers in accordance with the refereeing procedure of Pattern Recognition, and only those manuscripts that require minor revisions will be accepted for rapid publication in this special issue.
Important Dates:
[*]Paper Submission: March 1, 2016
[*]First Notification: June 15, 2016
[*]Revised Manuscript: September 1, 2016
[*]Notification of Acceptance: November 1, 2016
[*]Final Manuscript Due: December 1, 2016
[*]Publication Date: First Quarter of 2017
Guest Editors:
[*]Tao Mei, Microsoft Research, China (tmei@microsoft.com)
[*]Jason Corso, University of Michigan, USA (jjcorso@eecs.umich.edu)
[*]Jiebo Luo, University of Rochester, USA (jluo@cs.rochester.edu)
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