Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organization in Biological Systems
Self-Organization in Biological Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Generalized Principal Component Analysis (GPCA)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Counting Crowded Moving Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Unsupervised Bayesian Detection of Independent Motion in Crowds
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Floor Fields for Tracking in High Density Crowd Scenes
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering of time series data-a survey
Pattern Recognition
Improving data association by joint modeling of pedestrian trajectories and groupings
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Object segmentation by long term analysis of point trajectories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models
International Journal of Computer Vision
Learning semantic scene models by trajectory analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Random field topic model for semantic region analysis in crowded scenes from tracklets
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance
IEEE Transactions on Circuits and Systems for Video Technology
Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Hi-index | 0.00 |
Coherent motions, which describe the collective movements of individuals in crowd, widely exist in physical and biological systems. Understanding their underlying priors and detecting various coherent motion patterns from background clutters have both scientific values and a wide range of practical applications, especially for crowd motion analysis. In this paper, we propose and study a prior of coherent motion called Coherent Neighbor Invariance, which characterizes the local spatiotemporal relationships of individuals in coherent motion. Based on the coherent neighbor invariance, a general technique of detecting coherent motion patterns from noisy time-series data called Coherent Filtering is proposed. It can be effectively applied to data with different distributions at different scales in various real-world problems, where the environments could be sparse or extremely crowded with heavy noise. Experimental evaluation and comparison on synthetic and real data show the existence of Coherence Neighbor Invariance and the effectiveness of our Coherent Filtering.