Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Compressive Sensing for Background Subtraction
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Efficient highly over-complete sparse coding using a mixture model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Robust principal component analysis?
Journal of the ACM (JACM)
Higher order contractive auto-encoder
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Evaluation of background subtraction techniques for video surveillance
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
LOST: Longterm Observation of Scenes (with Tracks)
WACV '12 Proceedings of the 2012 IEEE Workshop on the Applications of Computer Vision
Density-Based Multifeature Background Subtraction with Support Vector Machine
IEEE Transactions on Pattern Analysis and Machine Intelligence
Background modeling by subspace learning on spatio-temporal patches
Pattern Recognition Letters
Adaptive deconvolutional networks for mid and high level feature learning
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Background subtraction using low rank and group sparsity constraints
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Disentangling factors of variation for facial expression recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
A Prototype Learning Framework Using EMD: Application to Complex Scenes Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Streams of images from large numbers of surveillance webcams are available via the web. The continuous monitoring of activities at different locations provides a great opportunity for research on the use of vision systems for detecting actors, objects, and events, and for understanding patterns of activity and anomaly in real-world settings. In this work we show how images available on the web from surveillance webcams can be used as sensors in urban scenarios for monitoring and interpreting states of interest such as traffic intensity. We highlight the power of the cyclical aspect of the lives of people and of cities. We extract from long-term streams of images typical patterns of behavior and anomalous events and situations, based on considerations of day of the week and time of day. The analysis of typia and atypia required a robust method for background subtraction. For this purpose, we present a method based on sparse coding which outperforms state-of-the-art works on complex and crowded scenes.