Probabilistic Posture Classification for Human-Behavior Analysis

TLDR

Computer vision and widespread multimedia access enable largely automated human‑behavior analysis systems. The study proposes to analyze human behavior by classifying posture to detect events such as falls. The approach computes projection histograms per frame, compares them to probabilistic posture maps, and validates the result with a tracking module that also handles occlusions for indoor robustness. Experiments show over 95% accuracy in posture classification even under challenging conditions.

Abstract

Computer vision and ubiquitous multimedia access nowadays make feasible the development of a mostly automated system for human-behavior analysis. In this context, our proposal is to analyze human behaviors by classifying the posture of the monitored person and, consequently, detecting corresponding events and alarm situations, like a fall. To this aim, our approach can be divided in two phases: for each frame, the projection histograms (Haritaoglu et al., 1998) of each person are computed and compared with the probabilistic projection maps stored for each posture during the training phase; then, the obtained posture is further validated exploiting the information extracted by a tracking module in order to take into account the reliability of the classification of the first phase. Moreover, the tracking algorithm is used to handle occlusions, making the system particularly robust even in indoors environments. Extensive experimental results demonstrate a promising average accuracy of more than 95% in correctly classifying human postures, even in the case of challenging conditions.