Human‐motion recognition via a fuzzy associative memory system
Hirohide Ushida, Toru Yamaguchi, Tomohiro Takagi · Systems and Computers in Japan · 1995
Abstract Human‐motion recognition techniques so far present several problems, such as difficult knowledge representation and poor robustness. With the aim of achieving recognition of human motion in a robust way by using knowledge representation that is easy to be understood, this paper proposes a method utilizing a fuzzy associative memory system. In the proposed method, the fuzzy associative memory system embodies fuzzy inference rules which define motion patterns of characteristic states contained in the time‐series pattern representing the motion. In the fuzzy associative memory system, inference is carried out with state transition patterns as input, and motion recognition is performed. The inference in the fuzzy associative memory system has the following two characteristics: (1) since knowledge can be represented as inference rules, it is easy to be understood by humans; (2) even in ambiguous situations or when part of the input information is missing, the information can be restored, or the degree of ambiguity can be controlled at inference time, etc., by the bidirectional processing of the conditional and action parts of the rules, so that appropriate inference results are obtained. In the proposed method, the time‐series motion patterns are converted into state transition patterns, and by using an associative inference system with the aforementionedcharacteristics, it is possible to perform robust recognition. The effectiveness of the proposed method is demonstrated through on‐line experimental results for the motion of nonspecific individuals.