Recognition of long-term behaviors by parsing sequences of short-term actions with a stochastic regular grammar
conference paper
Human behavior understanding from visual data has applications such as threat recognition. A lot of approaches are restricted to limited time actions, which we call short-term actions. Long-term behaviors are sequences of short-term actions that are more extended in time. Our hypothesis is that they usually present some structure that can be exploited to improve recognition of short-term actions. We present an approach to model long-term behaviors using a syntactic approach. Behaviors to be recognized are hand-crafted into the model in the form of grammar rules. This is useful for cases when few (or no) training data is available such as in threat recognition. We use a stochastic parser so we handle noisy inputs. The proposed method succeeds in recognizing a set of predefined long-term interactions in the CAVIAR dataset. Additionally, we show how imposing prior knowledge about the structure of the long-term behavior improves the recognition of short-term actions with respect to standard statistical approaches.
TNO Identifier
463590
Source title
Joint IAPR International Workshops on Structural and Syntactic PatternRecognition, SSPR 2012 and Statistical Techniques in Pattern Recognition, SPR 2012, 7-9 November 2012, Hiroshima, Japan
Files
To receive the publication files, please send an e-mail request to TNO Repository.