Hierarchical Relaxed Partitioning System for Activity Recognition

Abstract

A hierarchical relaxed partitioning system (HRPS) is proposed for recognizing similar activities which has a feature space with multiple overlaps. Two feature descriptors are built from the human motion analysis of a 2-D stick figure to represent cyclic and noncyclic activities. The HRPS first discerns the pure and impure activities, i.e., with no overlaps and multiple overlaps in the feature space, respectively, then tackles the multiple overlaps problem of the impure activities via an innovative majority voting scheme. The results show that the proposed method robustly recognizes various activities of two different resolution data sets, i.e., low and high (with different views). The advantage of HRPS lies in the real-time speed, ease of implementation and extension, and nonintensive training.

Improved strategy for human action recognition; experiencing a cascaded design

“…The MuHAVi action recognition data set [49] is known as an advanced form of PETS and CAVIAR. The PETS and CAVIAR data sets are not developed for HAR.…”