NDLI: One-scan rule extraction to explain significant vehicle interactions with guaranteed error value

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One-scan rule extraction to explain significant vehicle interactions with guaranteed error value

Content Provider	ACM Digital Library
Author	Florez, Omar U. Dyreson, Curtis
Abstract	Counting frequent itemsets allows us to compute the importance of items over a stream of data. Translating this concept to video streams imposes the need of representing activities as a sequence of activities over a video stream. In this paper, we present a model to find approximate co-occurring associations between activities from video stream data considering an unsupervised clustering of activities. We show that a hierarchical Topic Model of two stochastic processes is needed to jointly learn both an unknown number of activities in the video and the visual features that positively correlate for each activity. Unlike most of previous works, we decouple the analysis of associations between multiple moving objects from the discovery of activities. While the discovery of activities is an off-line process in which event distributions are grouped, the discovery of rules is an on-line process that approximates the importance of each rule with guaranteed error value. Our method reduces space complexity by adapting the algorithm to the amount of memory available before any process to update frequency values for itemsets is incrementally performed. The most visible aspect of this effort is the incremental generation of rules that discover the interaction of frequent activities for current scenes. Our experimental results show that our approach efficiently and automatically discovers sets of activities in a video stream coming from surveillance videos containing complex traffic scenes governed by multiple semaphores, while evaluating their frequent occurrence and co-occurring relationships.
Starting Page	27
Ending Page	38
Page Count	12
File Format	PDF
ISSN	15596915 19310161
DOI	10.1145/2340416.2340419
Journal	ACM SIGAPP Applied Computing Review (SIAP)
Volume Number	12
Issue Number	2
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Scene discovery Statistical learning Video streams
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

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