Please use this identifier to cite or link to this item: https://doi.org/10.1109/TITS.2004.838219
Title: Framework for real-time behavior interpretation from traffic video
Authors: Kumar, P.
Ranganath, S. 
Weimin, H.
Sengupta, K.
Keywords: Bayesian network
Behavior analysis
Camera calibration
Classification
Context
Event detection
Three-dimensional (3-D) tracking
Tracking
Video
Issue Date: Mar-2005
Source: Kumar, P., Ranganath, S., Weimin, H., Sengupta, K. (2005-03). Framework for real-time behavior interpretation from traffic video. IEEE Transactions on Intelligent Transportation Systems 6 (1) : 43-53. ScholarBank@NUS Repository. https://doi.org/10.1109/TITS.2004.838219
Abstract: Video-based surveillance systems have a wide range of applications for traffic monitoring, as they provide more information as compared to other sensors. In this paper, we present a rule-based framework for behavior and activity detection in traffic videos obtained from stationary video cameras. Moving targets are segmented from the images and tracked in real time. These are classified into different categories using a novel Bayesian network approach, which makes use of image features and image-sequence-based tracking results for robust classification. Tracking and classification results are used in a programmed context to analyze behavior. For behavior recognition, two types of interactions have mainly been considered. One is interaction between two or more mobile targets in the field of view (FoV) of the camera. The other is interaction between targets and stationary objects in the environment. The framework is based on two types of a priori information: 1) the contextual information of the camera's FoV, in terms of the different stationary objects in the scene and 2) sets of predefined behavior scenarios, which need to be analyzed in different contexts. The system can recognize behavior from videos and give a lexical output of the detected behavior. It also is capable of handling uncertainties that arise due to errors in visual signal processing. We demonstrate successful behavior recognition results for pedestrian-vehicle interaction and vehicle-checkpost interactions. © 2005 IEEE.
Source Title: IEEE Transactions on Intelligent Transportation Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/70371
ISSN: 15249050
DOI: 10.1109/TITS.2004.838219
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

118
checked on Dec 7, 2017

WEB OF SCIENCETM
Citations

89
checked on Nov 22, 2017

Page view(s)

11
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.