Name
Title | ||
|---|---|---|
Task Prediction in Cooking Activities Using Hierarchical State Space Markov Chain and Object Based Task Grouping |
Abstract | ||
|---|---|---|
Cooking activities are complex activities consisting of multiple steps or tasks. These tasks can be associated with one another based on two properties - the temporal structure that defines the sequence of occurrence of tasks and the objects that are used in the activity. This paper develops cooking activity models for the purpose of task prediction based on these two properties. The temporal structure of the sequence of tasks is captured by the novel hierarchical state space markov chain (HMC) and the object usage is represented using the object based task group (OTG) models. A probabilistic task prediction algorithm that fuses the HMC and OTG models has been developed to predict the next most probable task, given that a sequence of tasks has been completed. The proposed models and algorithms have been evaluated on two complex cooking activities - making brownies and making eggs, achieving a subject independent accuracy of 68.5% for predicting tasks, which is an improvement by an average of 6% in comparison to a Markov chain. The work done in the paper is first of its kind as it focuses on task prediction rather than task recognition. The task prediction framework described in the paper can be easily adapted to any complex activity supporting various annotation schemes and activity models. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ISM.2010.49 | Multimedia |
Keywords | Field | DocType |
cooking activities,task prediction,activity model,probabilistic task prediction algorithm,task group,complex activity,task recognition,hierarchical state space markov,temporal structure,probable task,markov chain,task grouping,task prediction framework,training data,prediction algorithms,state space,data models,markov processes,hidden markov models,data structures,accuracy,predictive models | Data structure,Data modeling,Markov process,Pattern recognition,Computer science,Markov chain,Artificial intelligence,Probabilistic logic,Fuse (electrical),Hidden Markov model,State space,Machine learning | Conference |
ISBN | Citations | PageRank |
978-0-7695-4217-1 | 3 | 0.47 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prasanth Lade | 1 | 37 | 5.37 |
| Narayanan C. Krishnan | 2 | 392 | 17.46 |
| Sethuraman Panchanathan | 3 | 1431 | 152.04 |