This paper presents a methodology for automatic segmentation and recognition of continuous human activity. We segment a continuous human activity into separate actions and correctly identify each action. The camera views the subject from the lateral view: there are no distinct breaks or pauses between the execution of different actions. We have no prior knowledge about the commencement or termination of each action. We compute the angles subtended by three major components of the body with the vertical axis, namely the torso, the upper component of the leg and the lower component of the leg. Using these three angles as a feature vector we classify frames into breakpoint and non-breakpoint frames. Breakpoints indicate an action's commencement or termination. We use single action sequences for the training data set. The test sequences, on the other hand are continuous sequences of human activity that consist of three or more actions in succession. The system has been tested on continuous activity sequences containing actions such as walking, sitting down, standing up, bending, getting up, squatting and rising. It detects the breakpoints and classifies the actions between them.
Segmentation and recognition of continuous human activity
2001-01-01
1476877 byte
Conference paper
Electronic Resource
English
Segmentation and Recognition of Continuous Human Activity
British Library Conference Proceedings | 2001
|Recovering Human Body Configurations: Combining Segmentation and Recognition
British Library Conference Proceedings | 2004
|Posture Recognition and Segmentation from 3D Human Body Scans
British Library Conference Proceedings | 2002
|Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models
British Library Online Contents | 2011
|