Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
View Invariance for Human Action Recognition
International Journal of Computer Vision
Local velocity-adapted motion events for spatio-temporal recognition
Computer Vision and Image Understanding
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Searching for Complex Human Activities with No Visual Examples
International Journal of Computer Vision
Cross-View Action Recognition from Temporal Self-similarities
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Semantic Representation and Recognition of Continued and Recursive Human Activities
International Journal of Computer Vision
On hierarchical modelling of motion for workflow analysis from overhead view
Machine Vision and Applications
| Hi-index | 0.01 |
We consider the problem of developing an automated visual solution for detecting human activities within industrial environments. This has been performed using an overhead view. This view was chosen over more conventional oblique views as it does not suffer from occlusion, but still retains powerful cues about the activity of individuals. A simple blob tracker has been used to track the most significant moving parts i.e. human beings. The output of the tracking stage was manually labelled into 4 distinct categories: walking; carrying; handling and standing still which are taken together from the basic building blocks of a higher work flow description. These were used to train a decision tree using one subset of the data. A separate training set is used to learn the patterns in the activity sequences by Hidden Markov Models (HMM). On independent testing, the HMM models are applied to analyse and modify the sequence of activities predicted by the decision tree.