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In this paper, we introduce a new hierarchical model for human actionrecognition using body joint locations. Our model can categorize complexactions in videos, and perform spatio-temporal annotations of the atomicactions that compose the complex action being performed.That is, for eachatomic action, the model generates temporal action annotations by estimatingits starting and ending times, as well as, spatial annotations by inferring thehuman body parts that are involved in executing the action. our model includesthree key novel properties: (i) it can be trained with no spatial supervision,as it can automatically discover active body parts from temporal actionannotations only; (ii) it jointly learns flexible representations for motionposelets and actionlets that encode the visual variability of body parts andatomic actions; (iii) a mechanism to discard idle or non-informative body partswhich increases its robustness to common pose estimation errors. We evaluatethe performance of our method using multiple action recognition benchmarks. Ourmodel consistently outperforms baselines and state-of-the-art actionrecognition methods.

A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets