Two-step learning about normal and exceptional human behaviors incorporating patterns and knowledge
Gi Hyun Lim · 2016
Human activity recognition, especially exceptional activity recognition has been regarded as an important aspect in intelligent service robotics. Several challenges in activity recognition - unexpected and untypical exceptional behaviors, a small but growing number of training examples - make it hard to solve this problem. Despite the variety of human behaviors, there are some normal patterns, especially scenario-oriented human activities. This paper presents an incremental learning method for exceptional behavior patterns based on prerequisites. The proposed method models the normal activities as prerequisites from several demonstrations following a given scenario, and learns autonomously and incrementally new exceptional activities, which may not follow the scenario. Case studies show that the proposed method can gradually improve the recognition rate, and incrementally learn new exceptional human activities.