NDLI: Learning Semantic Motion Patterns for Dynamic Scenes by Improved Sparse Topical Coding

Content Provider	IEEE Xplore Digital Library
Author	Wei Fu Jinqiao Wang Zechao Li Hanqing Lu Songde Ma
Copyright Year	2012
Abstract	With the proliferation of cameras in public areas, it becomes increasingly desirable to develop fully automated surveillance and monitoring systems. In this paper, we propose a novel unsupervised approach to automatically explore motion patterns occurring in dynamic scenes under an improved sparse topical coding (STC) framework. Given an input video with a fixed camera, we first segment the whole video into a sequence of clips (documents) without overlapping. Optical flow features are extracted from each pair of consecutive frames, and quantized into discrete visual words. Then the video is represented by a word-document hierarchical topic model through a generative process. Finally, an improved sparse topical coding approach is proposed for model learning. The semantic motion patterns (latent topics) are learned automatically and each video clip is represented as a weighted summation of these patterns with only a few nonzero coefficients. The proposed approach is purely data-driven and scene independent (not an object-class specific), which make it suitable for very large range of scenarios. Experiments demonstrate that our approach outperforms the state-of-the art technologies in dynamic scene analysis.
Starting Page	296
Ending Page	301
File Size	1437493
Page Count	6
File Format	PDF
ISBN	9781467316590
ISSN	19457871
DOI	10.1109/ICME.2012.133
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-09
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semantics Dictionaries Encoding Visualization Computational modeling Dynamics Cameras scene model motion patterns sparse topical coding
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Discovering motion patterns in traffic videos using improved Group Sparse Topical Coding

Beyond bag-of-words: An improved Sparse Topical Coding for learning motion patterns in traffic scenes

Learning semantic visual dictionaries: A new method for local feature encoding

Learning sparse covariance patterns for natural scenes

Beyond SIFT for image classification

Natural Scene Retrieval Based on Non-negative Sparse Coding

Unusual Scene Detection Using Distributed Behaviour Model and Sparse Representation

Leveraging Human Fixations in Sparse Coding: Learning a Discriminative Dictionary for Saliency Prediction

Learning sparse dictionaries for saliency detection

Learning Semantic Motion Patterns for Dynamic Scenes by Improved Sparse Topical Coding

Similar Documents

Discovering motion patterns in traffic videos using improved Group Sparse Topical Coding

Beyond bag-of-words: An improved Sparse Topical Coding for learning motion patterns in traffic scenes

Learning semantic visual dictionaries: A new method for local feature encoding

Learning sparse covariance patterns for natural scenes

Beyond SIFT for image classification

Natural Scene Retrieval Based on Non-negative Sparse Coding

Unusual Scene Detection Using Distributed Behaviour Model and Sparse Representation

Leveraging Human Fixations in Sparse Coding: Learning a Discriminative Dictionary for Saliency Prediction

Learning sparse dictionaries for saliency detection

Learning Semantic Motion Patterns for Dynamic Scenes by Improved Sparse Topical Coding