ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A scalable modular convex solver for regularized risk minimization
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
A stochastic graph evolution framework for robust multi-target tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Human activity analysis: A review
ACM Computing Surveys (CSUR)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A large-scale benchmark dataset for event recognition in surveillance video
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Discriminative Latent Models for Recognizing Contextual Group Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A "string of feature graphs" model for recognition of complex activities in natural videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper, we propose a mathematical framework to model activities with both motion and context information for activity recognition. This is motivated from the observations that an activity does not only depend on the motion of the objects of interest but the surrounding objects also provide useful cues for an understanding of the activity. Thus the surrounding objects can serve as context for the concerned activity. Given training data, our model aims to automatically capture and weigh motion and context patterns for each activity class, from sets of predefined attributes, during the learning process. Then, the learned model is used to generate optimum labels for activities in the testing videos based on the motion and context features of these activities. We show how to learn the model parameters via an unconstrained convex optimization methodology and how to predict the correct label for a testing instance. We show promising results on the publicly available VIRAT Ground Dataset that demonstrates the benefit of modeling the surrounding context in recognizing activities in a wide-area scene.