Recognizing constrained 3D human motion: An inference approach

Enormous uncertainties in unconstrained human motions lead to a fundamental challenge that many recognising algorithms have to face in practice: motion recognition has to be efficiently correct, but verifying whether or not the algorithm is robustly following the true target motion tends to be demanding, especially when human kinematic motions heavily overlap and occlusion occurs. Due to the lack of a good solution to this problem, many existing methods tend to be either effective but computationally intensive or efficient but vulnerable to false alarms. This chapter presents a novel inference engine for recognising occluded 3D human motion assisted by the recognition context. First, uncertainties are wrapped into a fuzzy membership function via a novel method called fuzzy quantile generation which employs metrics derived from the probabilistic quantile function. Then, time-dependent and context-aware rules are produced via a genetic programming to smooth the qualitative outputs represented by fuzzy membership functions. Finally, occlusion in motion recognition is taken care of by introducing new procedures for feature selection and feature reconstruction. Experimental results demonstrate the effectiveness of the proposed inference engine for 3D occluded human motion recognition.