A cost minimization approach to human behavior recognition
Gita Sukthankar, Katia P. Sycara · 2005
This paper presents a cost minimization approach to the problem of human behavior recognition. Using full-body motion capture data acquired from human subjects, our system recognizes the behaviors that a human subject is performing from a set of military maneuvers, based on the subject's motion type and proximity to landmarks. Low-level motion classification is performed using support vector machines (SVMs) and a hidden Markov Model (HMM); output from the classifier is used as an input feature for the behavior recognizer. Given the dynamic and highly reactive nature of the domain, our system must handle behavior sequences that are frequently interrupted and often interleaved. To recognize such behavior sequences, we employ dynamic programming in conjunction with a behavior transition cost function to efficiently select the most parsimonious explanation for the human's actions. We demonstrate that our system is robust to action classification errors and deviations by the human subject from the expected set of behaviors. Our approach is well suited for incorporation into synthetic agents that cooperate or compete against human subjects in virtual reality training environments.