Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
MobiMine: monitoring the stock market from a PDA
ACM SIGKDD Explorations Newsletter
The Haar Wavelet Transform in the Time Series Similarity Paradigm
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Exact indexing of dynamic time warping
Knowledge and Information Systems
Personal Heart Monitoring System Using Smart Phones To Detect Life Threatening Arrhythmias
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A Weighted Moving Average-based Approach for Cleaning Sensor Data
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Efficient indexing methods for probabilistic threshold queries over uncertain data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Ranked subsequence matching in time-series databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Model based human motion tracking using probability evolutionary algorithm
Pattern Recognition Letters
Efficient Similarity Join of Large Sets of Moving Object Trajectories
TIME '08 Proceedings of the 2008 15th International Symposium on Temporal Representation and Reasoning
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Motion Sequence-Based Human Abnormality Detection Scheme for Smart Spaces
Wireless Personal Communications: An International Journal
Hough Forests for Object Detection, Tracking, and Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of human actions using texture descriptors
Machine Vision and Applications - Special Issue on Dynamic Textures in Video
Engineering Applications of Artificial Intelligence
3D human motion analysis framework for shape similarity and retrieval
Image and Vision Computing
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With the recent development of ubiquitous technologies, many new applications have been emerging for smart home implementation. Usually, such applications are based on diverse sensors. One fundamental operation in the applications is to find out semantically meaningful events or activities from huge sensor data stream. Usually, such event or activity is represented by a salient sequence pattern. Among the diverse research issues, detecting salient sequence patterns of human motions from image sensor data stream has received much attention for security and surveillance purposes. In the case of detecting human motions from image sensor data, finding and matching their salient sequence patterns could become more complicated since semantically same motions could show diverse variations such as different motion time. Based on this observation, in this paper, we propose a new querying and answering scheme for continuous sensor data stream to detect abnormal human motions. More specifically, we first present a new hierarchical querying scheme to consider variable length of semantically same human motions. Secondly, we present an indexing scheme to efficiently find semantically meaningful motion sequences in the sensor data stream. Thirdly, we present Dynamic Group Warping algorithm to effectively filter out unnecessary human motions. Through extensive experiments, we show that our proposed method achieves outstanding performance.