Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Automatic symbolic traffic scene analysis using belief networks
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Automatic parsing and indexing of news video
Multimedia Systems
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
An Unsupervised Segmentation Framework For Texture Image Queries
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
Story Segmentation and Detection of Commercials in Broadcast News Video
ADL '98 Proceedings of the Advances in Digital Libraries Conference
Object tracking and multimedia augmented transition network for video indexing and modeling
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Mining positive and negative association rules: an approach for confined rules
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Video Data Mining: Semantic Indexing and Event Detection from the Association Perspective
IEEE Transactions on Knowledge and Data Engineering
Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event n-Grams
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Mining rare and frequent events in multi-camera surveillance video using self-organizing maps
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
An APRIORI-based Method for Frequent Composite Event Discovery in Videos
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
CoMMA: a framework for integrated multimedia mining using multi-relational associations
Knowledge and Information Systems
Sequential association mining for video summarization
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Unusual Event Detection via Multi-camera Video Mining
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Semantic Analysis and Video Event Mining in Sports Video
AINAW '08 Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops
Mining Sequential Patterns with Negative Conclusions
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Hierarchical Temporal Association Mining for Video Event Detection in Video Databases
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Real time video data mining for surveillance video streams
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Structural and event based multimodal video data modeling
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
Traffic monitoring and accident detection at intersections
IEEE Transactions on Intelligent Transportation Systems
Association and Temporal Rule Mining for Post-Filtering of Semantic Concept Detection in Video
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia
Hi-index | 12.05 |
In the UK alone there are currently over 4.2 million operational CCTV cameras, that is virtually one camera for every 14th person, and this figure is increasing at a fast rate throughout the world (especially after the tragic events of 9/11 and 7/7) (Norris, McCahill, & Wood, 2004). Security concerns are not the only factor driving the rapid growth of CCTV cameras. Another important reason is the access of hidden knowledge extracted from CCTV footage to be used for effective business decision making, such as store designing, customer services, product marketing, reducing store shrinkage, etc. Events occurring in observed scenes are one of the most important semantic entities that can be extracted from videos (Anwar & Naftel, 2008). Most of the work presented in the past is based upon finding frequent event patterns or deals with discovering already known abnormal events. In contrast, in this paper we present a framework to discover unknown anomalous events associated with a frequent sequence of events (A"E"A"S"P); that is to discover events, which are unlikely to follow a frequent sequence of events. This information can be very useful for discovering unknown abnormal events and can provide early actionable intelligence to redeploy resources to specific areas of view (such as PTZ camera or attention of a CCTV user). Discovery of anomalous events against a sequential pattern can also provide business intelligence for store management in the retail sector. The proposed event mining framework is an extension to our previous research work presented in Anwar et al. (2010) and also takes the temporal aspect of anomalous events against frequent sequence of events into consideration, that is to discover anomalous events which are true for a specific time interval only and might not be an anomalous events against frequent sequence of events over a whole time spectrum and vice versa. To confront the memory expensive process of searching all the instances of multiple sequential patterns in each data sequence an efficient dynamic sequential pattern search mechanism is introduced. Different experiments are conducted to evaluate the proposed anomalous events against frequent sequence of events mining algorithm's accuracy and performance.