Tools for semi-automatic monitoring of industrial workflows

This paper describes a tool chain for monitoring complex workflows. Statistics obtained from automatic workflow monitoring in a car assembly environment assist in improving industrial safety and process quality. To this end, we propose automatic detection and tracking of humans and their activity in multiple networked cameras. The described tools offer human operators retrospective analysis of a huge amount of pre-recorded and analyzed footage from multiple cameras in order to get a comprehensive overview of the workflows. Furthermore, the tools help technical administrators in adjusting algorithms by letting the user correct detections (for relevance feedback) and ground truth for evaluation. Another important feature of the tool chain is the capability to inform the employees about potentially risky conditions using the tool for automatic detection of unusual scenes.

[1]  Werner Bailer,et al.  SAM: an interoperable metadata model for multimodal surveillance applications , 2009, Defense + Commercial Sensing.

[2]  G. J. Smith,et al.  Behind the Screens: Examining Constructions of Deviance and Informal Practices among CCTV Control Room Operators in the UK , 2002 .

[3]  Marcus Thaler,et al.  Improving Person Detection in Videos by Automatic Scene Adaptation , 2010, VISAPP.

[4]  Marcus Thaler,et al.  Automatic Inter-image Homography Estimation from Person Detections , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[5]  Herbert Jaeger,et al.  The''echo state''approach to analysing and training recurrent neural networks , 2001 .

[6]  Theodora A. Varvarigou,et al.  Multi-agent Based Surveillance of Workflows , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[7]  Luc Van Gool,et al.  Exploring context to learn scene specific object detectors , 2009 .

[8]  Theodora A. Varvarigou,et al.  Workflows Recognition through Multi Agents in Surveillance Systems , 2010, IFIP AI.

[9]  M. Lee,et al.  The University of Southampton Multi-Biometric Tunnel and introducing a novel 3D gait dataset , 2008, 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems.

[10]  Theodora A. Varvarigou,et al.  An architecture for a self configurable video supervision , 2008, AREA '08.

[11]  Luc Van Gool,et al.  Cascaded Confidence Filtering for Improved Tracking-by-Detection , 2010, ECCV.