BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A foundation for representing and querying moving objects
ACM Transactions on Database Systems (TODS)
A survey of free-form object representation and recognition techniques
Computer Vision and Image Understanding
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Supporting Movement Pattern Queries in User-Specified Scales
IEEE Transactions on Knowledge and Data Engineering
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Location-based spatial queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Range and kNN query processing for moving objects in grid model
Mobile Networks and Applications
SINA: scalable incremental processing of continuous queries in spatio-temporal databases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
STRIPES: an efficient index for predicted trajectories
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Clustering time series from ARMA models with clipped data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast mining of spatial collocations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A partial join approach for mining co-location patterns
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Monitoring k-Nearest Neighbor Queries over Moving Objects
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Continuous nearest neighbor search
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Continuous K-nearest neighbor queries for continuously moving points with updates
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Query and update efficient B+-tree based indexing of moving objects
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Nearest neighbor search on moving object trajectories
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Feature Extraction from Sensor Data Streams for Real-Time Human Behaviour Recognition
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Object tracking with self-updating tracking window
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
Anomaly detection for travelling individuals with cognitive impairments
ACM SIGACCESS Accessibility and Computing
Privacy-preserving trajectory data publishing by local suppression
Information Sciences: an International Journal
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With recent advances in sensory and mobile computing technology, enormous amounts of data about moving objects are being collected. With such data, it becomes possible to automatically identify suspicious behavior in object movements. Anomaly detection in massive sets of moving objects has many important applications, especially in surveillance, law enforcement, and homeland security. Due to the sheer volume of spatiotemporal and non-spatial data (such as weather and object type) associated with moving objects, it is challenging to develop a method that can efficiently and effectively detect anomalies in complex scenarios. The problem is further complicated by the fact that anomalies may occur at various levels of abstraction and be associated with different time and location granularities. In this paper, we analyze the problem of anomaly detection in moving objects and propose an efficient and scalable classification method, Motion-Alert, which proceeds with the following three steps. Object movement features, called motifs, are extracted from the object paths. Each path consists of a sequence of motif expressions, associated with the values related to time and location. To discover anomalies in object movements, motif-based generalization is performed that clusters similar object movement fragments and generalizes the movements based on the associated motifs. With motif-based generalization, objects are put into a multi-level feature space and are classified by a classifier that can handle high-dimensional feature spaces. We implemented the above method as one of the core components in our moving-object anomaly detection system, motion-alert. Our experiments show that the system is more accurate than traditional classification techniques.