Object trajectory clustering via tensor analysis

Authors:
Huiyu Zhou;Dacheng Tao;Yuan Yuan;Xuelong Li
Affiliations:
Brunel University, Uxbridge, Middlesex, United Kingdom;School of Computer Engineering, Nanyang Technological University, Singapore;Aston University, Birmingham, United Kingdom;Xi'an Institute of Optics and Precision Mechanics, CAS, Xi'an, China
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 7
Cited 3

Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Adaptive Tracking to Classify and Monitor Activities in a Site

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Rank-R Approximation of Tensors: Using Image-as-Matrix Representation

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Supervised tensor learning

Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Locally Linear Embedding With High-Order Tensor Data

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multifeature Object Trajectory Clustering for Video Analysis

IEEE Transactions on Circuits and Systems for Video Technology

Spectral clustering with eigenvector selection based on entropy ranking

Neurocomputing
Human action segmentation and recognition via motion and shape analysis

Pattern Recognition Letters
A template matching approach of one-shot-learning gesture recognition

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a new video object trajectory clustering algorithm, which allows us to model and analyse the patterns of object behaviors based on the extracted features using tensor analysis. The proposed algorithm consists of three steps as follows: extraction of trajectory features by tensor analysis, non-parametric probabilistic mean shift clustering and clustering correction. The performance of the proposed algorithm is evaluated on standard data-sets and compared with classical techniques.