This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication

Year(s) from:  to 
Keywords (separated by spaces):

Online classification of visual tasks for industrial workflow monitoring

A. Voulodimos, D. Kosmopoulos, G. Veres, H. Grabner, L. Van Gool, T. Varvarigou,
Neural Networks


Modelling and classification of time series stemming from visual workflows is a very challenging problem due to the inherent complexity of the activity patterns involved and the difficulty in tracking moving targets. In this paper, we propose a framework for classification of visual tasks in industrial environments. We propose a novel method to automatically segment the input stream and to classify the resulting segments using prior knowledge and hidden Markov models (HMMs), combined through a genetic algorithm. We compare this method to an echo state network (ESN) approach, which is appropriate for general-purpose time-series classification. In addition, we explore the applicability of several fusion schemes for multicamera configuration in order to mitigate the problem of limited visibility and occlusions. The performance of the suggested approaches is evaluated on real-world visual behaviour scenarios.

Download in pdf format
  author = {A. Voulodimos and D. Kosmopoulos and G. Veres and H. Grabner and L. Van Gool and T. Varvarigou and},
  title = {Online classification of visual tasks for industrial workflow monitoring},
  journal = {Neural Networks},
  year = {2011},
  month = {},
  pages = {},
  volume = {},
  number = {},
  keywords = {}