Skeletal Action Recognition with Local Joint Perimeter Maps Learned Using Deep Metric Embedding

Developing a highly accurate 3D action recognition framework using traditional feature maps on top of conventional neural networks has been limited by inter class discriminations of similar looking action sequences. We approach this problem through local joint Perimeter maps (LJPM) on skeletal action datasets that are learned by deep metric learning (DML) process. We propose to solve gaps in training pipelines to attain higher accuracies using a feature embedding space which is learned using the triplet loss function. To test our approach, we applied our 3D motion captured action dataset, KLHA3D-102 and two other benchmarks, HDM05 and NTU RGB D. The results obtained show that the embedding features performed better due to triplet loss which maximized the separation between multiple classes similar features. Further it the triplet loss embedding has minimal false positive effects on 3D skeletal action data recognition tasks.