Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Information Retrieval
Layered representations for learning and inferring office activity from multiple sensory channels
Computer Vision and Image Understanding - Special issue on event detection in video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Propagation networks for recognition of partially ordered sequential action
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
On supervised human activity analysis for structured environments
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Automatic workflow monitoring in industrial environments
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Bayesian filter based behavior recognition in workflows allowing for user feedback
Computer Vision and Image Understanding
Workflow activity monitoring using dynamics of pair-wise qualitative spatial relations
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Hi-index | 0.00 |
Understanding human behaviour is a high level perceptual problem, one which is often dominated by the contextual knowledge of the environment, and where concerns such as occlusion, scene clutter and high within-class variations are commonplace. Nonetheless, such understanding is highly desirable for automated visual surveillance. We consider this problem in a context of a workflow analysis within an industrial environment. The hierarchical nature of the workflow is exploited to split the problem into `activity' and `task' recognition. In this, sequences of low level activities are examined for instances of a task while the remainder are labelled as background. An initial prediction of activity is obtained using shape and motion based features of the moving blob of interest. A sequence of these activities is further adjusted by a probabilistic analysis of transitions between activities using hidden Markov models (HMMs). In task detection, HMMs are arranged to handle the activities within each task. Two separate HMMs for task and background compete for an incoming sequence of activities. Imagery derived from a camera mounted overhead the target scene has been chosen over the more conventional oblique views (from the side) as this view does not suffer from as much occlusion, and it poses a manageable detection and tracking problem while still retaining powerful cues as to the workflow patterns. We evaluate our approach both in activity and task detection on a challenging dataset of surveillance of human operators in a car manufacturing plant. The experimental results show that our hierarchical approach can automatically segment the timeline and spatially localize a series of predefined tasks that are performed to complete a workflow.