Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Training conditional random fields via gradient tree boosting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
TemporalBoost for Event Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting motion features for visual human activity representation
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Optical Flow Based Detection in Mixed Human Robot Environments
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
ISROBOTNET: a testbed for sensor and robot network systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Feature set search space for fuzzyboost learning
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
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We present a novel boosting algorithm where temporal consistency is addressed in a short-term way. Although temporal correlation of observed data may be an important cue for classification (e.g. of human activities) it is seldom used in boosting techniques. The recently proposed Temporal AdaBoost addresses the same problem but in a heuristic manner, first optimizing the weak learners without temporal integration. The classifier responses for past frames are then averaged together, as long as the total classification error decreases. We extend the GentleBoost algorithm by modeling time in an explicit form, as a new parameter during the weak learner training and in each optimization round. The time consistency model induces a fuzzy decision function, dependent on the temporal support of a feature or data point, with added robustness to noise. Our temporal boost algorithm is further extended to cope with multi class problems, following the JointBoost approach introduced by Torralba et. al. We can thus (i) learn the parameters for all classes at once, and (ii) share features among classes and groups of classes, both in a temporal and fully consistent manner. Finally, the superiority of our proposed framework is demonstrated comparing it to state of the art, temporal and non-temporal boosting algorithms. Tests are performed both on synthetic and 2 real challenging datasets used to recognize a total of 12 different human activities.