NDLI: Detecting anomaly using the scene modeling based on time delayed statistical data

Content Provider	IEEE Xplore Digital Library
Author	Lixin Chen Huiwen Guo Min Wang Yen-Lun Chen Xinyu Wu Wei Feng
Copyright Year	2015
Description	Author affiliation: Key Lab. of Human-Machine Intell.-Synergic Syst., Shenzhen Inst. of Adv. Technol., Shenzhen, China (Lixin Chen; Huiwen Guo; Min Wang; Yen-Lun Chen; Xinyu Wu; Wei Feng)
Abstract	We propose a novel approach for the crowd anomaly detection in multiple cameras with non-overlapping view. In this paper, we refer to the activities of crowd in far-field scenes. Firstly, we present a model for learning all of the motion patterns under single camera view, which are regarded as the normal situation. In the surveillance region, we mark the entrances and exits under the single camera view and acquire the crowd flow model by the K-means clustering algorithm. Secondly, we analyze the crowd flow model based on the time delayed statistical data between two camera views. And then we acquire the relative location among the entrances and exits in the different regions. Thirdly, we analyze the crowd transferring probabilistic model on the global scene based on the log-likelihood function and Dirichlet distribution to detect the crowd anomaly. We set up the empirical threshold value of probability e P . If the probability of detected model is less than e P , the detected model is marked as the crowd anomaly. Our approach is evaluated on the simulated data set and the real data set in far-field scenes. Experimental results show the anomaly detection is precise.
Starting Page	871
Ending Page	876
File Size	808783
Page Count	6
File Format	PDF
e-ISBN	9781467391047
DOI	10.1109/ICInfA.2015.7279408
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Analytical models Crowd anomaly detection log-likelihood function Clustering algorithms Cameras Probabilistic logic Data models K-means clustering algorithm Trajectory Dirichlet distribution non-overlapping view
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

Using a Dynamic K-means Algorithm to Detect Anomaly Activities

Perception of emotions from crowd dynamics

Real-Time Anomaly Detection in Streams of Execution Traces

Crowd analysis with target tracking, K-means clustering and hidden Markov models

Intrusion detection based on MinMax K-means clustering

Research and Implementation of an Anomaly Detection Model Based on Clustering Analysis

Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes (2010)

Anomaly detection using firefly harmonic clustering algorithm

Pre-clustering algorithm for anomaly detection and clustering that uses variable size buckets

Detecting anomaly using the scene modeling based on time delayed statistical data

Similar Documents

Using a Dynamic K-means Algorithm to Detect Anomaly Activities

Perception of emotions from crowd dynamics

Real-Time Anomaly Detection in Streams of Execution Traces

Crowd analysis with target tracking, K-means clustering and hidden Markov models

Intrusion detection based on MinMax K-means clustering

Research and Implementation of an Anomaly Detection Model Based on Clustering Analysis

Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes (2010)

Anomaly detection using firefly harmonic clustering algorithm

Pre-clustering algorithm for anomaly detection and clustering that uses variable size buckets

Detecting anomaly using the scene modeling based on time delayed statistical data