Boosting with Temporal Consistent Learners: An Application to Human Activity Recognition

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...

• Type: Conference
• Year: 2007
• Where: ISVC
• Authors: Pedro Canotilho Ribeiro, Plinio Moreno, José Santos-Victor
• Keywords: Boosting Algorithms, ISVC 2007, Temporal Consistency, Temporal Correlation