Activity recognition based on semantic spatial relation

Lingxun Meng, Laiyun Qing, Peng Yang, Jun Miao, Xilin Chen, Dimitris N. Metaxas

Abstract: We propose an approach to recognize group activities which involve several persons based on modeling the interactions between human bodies. Benefitted from the recent progress in pose estimation [1], we model the activities as the interactions between the parts belong to the same person (intra-person) and those between the parts of different persons (inter-person). Then a unified, discriminative model which integrates both types of interactions is developed. The experiments on the UT-Interaction Dataset [2] show the promising results and demonstrate the power of the interacting models.