Abstract-We present a technique to classify human actions that involve object manipulation. Our focus is to accurately distinguish between actions that are related in that the object's state changes define the essential differences. Our algorithm uses a latent variable conditional random field that allows for the modelling of spatio-temporal relationships between the human motion and the corresponding object state changes. Our approach involves a factored representation that better allows for the description of causal effects in the way human action causes object state changes. The utility of incorporating such structure in our model is that it enables more accurate classification of activities that could enable robots to reason about interaction, and to learn using a high level vocabulary that captures phenomena of interest. We present experiments involving the recognition of human actions, where we show that our factored representation achieves superior performance in comparison to alternate flat representations.
Vafeias et al. REF use a Hidden-state CRF to model human motion and object state changes jointly, this time for the sole purpose of activity recognition.
Joint classification of actions and object state changes with a latent variable discriminative model
2014 IEEE International Conference on Robotics and Automation (ICRA)
2014 IEEE International Conference on Robotics and Automation (ICRA)
Mathematics Computer Science