Applied multivariate statistical analysis
Applied multivariate statistical analysis
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Self-organizing maps
Parameterized modeling and recognition of activities
Computer Vision and Image Understanding
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Content-Based Retrieval using Trajectories of Moving Objects in Video Databases
DASFAA '01 Proceedings of the 7th International Conference on Database Systems for Advanced Applications
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Application of the Self-Organizing Map to Trajectory Classification
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Extraction and Clustering of Motion Trajectories in Video
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A hybrid system for affine-invariant trajectory retrieval
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Evaluation of Matching Metrics for Trajectory-Based Indexing and Retrieval of Video Clips
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Discovering clusters in motion time-series data
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Trajectory-Based video retrieval for multimedia information systems
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
Content-trajectory approach for searching video databases
IEEE Transactions on Multimedia
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Learning activity patterns using fuzzy self-organizing neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Models for motion-based video indexing and retrieval
IEEE Transactions on Image Processing
Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models
IEEE Transactions on Image Processing
A fully automated content-based video search engine supporting spatiotemporal queries
IEEE Transactions on Circuits and Systems for Video Technology
MPEG-7 visual motion descriptors
IEEE Transactions on Circuits and Systems for Video Technology
A Multi-stage Competitive Neural Networks Approach for Motion Trajectory Pattern Learning
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Unsupervised discovery, modeling, and analysis of long term activities
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
Learning common behaviors from large sets of unlabeled temporal series
Image and Vision Computing
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This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal functional approximations. A Mahalanobis classifier is then used for the detection of anomalous trajectories. Motion trajectories are considered as time series and modeled using the leading Fourier coefficients obtained by a Discrete Fourier Transform. Trajectory clustering is then carried out in the Fourier coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Experiments are performed on two different datasets -- synthetic and pedestrian object tracking - to demonstrate the effectiveness of our approach. Applications to motion data mining in video surveillance databases are envisaged.