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Illustration of multi-camera tracking. Single-view detection and tracking results (left) are fused on the ground plane using the automatically estimated calibration information (center) for occlusion-free human person detection and tracking (right). 

Illustration of multi-camera tracking. Single-view detection and tracking results (left) are fused on the ground plane using the automatically estimated calibration information (center) for occlusion-free human person detection and tracking (right). 

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Figure 1 Illustration of typical tracking results using the cascaded...
Figure 2 Illustration of multi-camera tracking. Single-view detection...
Figure 3 Successful recognitions and failures over one working cycle. 
Figure 4 Output of the human activity detector. 
+3 Figure 5 Screenshot of the SCOVIS Data Browser (SDB) with different...
Tools for Semi-automatic Monitoring of Industrial Workflows
Conference Paper
Full-text available
  • Oct 2010
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 o...
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... calibration information is a prerequisite for multi-camera tracking. Figure 2 shows an example where single view tracking results from two cameras are fused on a ground plane using a constrained nearest neighbor clustering for obtaining occlusion free human detections. ...

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... An industrial monitoring problem is highly relevant and has been studied in several research projects. For instance, the SCOVIS project investigates the automatic work flow monitoring in a car assembly environment in order to improve safety and process quality (Kosmopoulos et al., 2012;Mörzinger et al., 2010;Voulodimos et al., 2011). ...
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... In addition, the task of real-time work-flow recognition can be achieved by a simple scene descriptor and an efficient time series analysis method [34]. Finally, there are approaches [25,26] that focus only on the recognition of assembly parts in manufacturing lines. ...
Efficient tracking using a robust motion estimation technique
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  • MULTIMED TOOLS APPL
Camera based supervision is a critical part of event detection and analysis applications. However, visual tracking still remains one of the biggest challenges in the area of computer vision, although it has been extensively discussed during in the previous years. In this paper we propose a robust tracking approach based on object flow, which is a motion model for estimating both the displacement and the direction of an object of interest. In addition, an observation model that utilizes a generative prior is adopted to tackle the pitfalls that derive from the appearance changes of the object under study. The efficiency of our technique is demonstrated using sequences captured in a complex industrial environment. The experimental results show that the proposed algorithm is sound, yielding improved performance in comparison with other tracking approaches.
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A Taxonomy for Combining Activity Recognition and Process Discovery in Industrial Environments: 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II
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Despite the increasing automation levels in an Industry 4.0 scenario, the tacit knowledge of highly skilled manufacturing workers remains of strategic importance. Retaining this knowledge by formally capturing it is a challenge for industrial organisations. This paper explores research on automatically capturing this knowledge by using methods from activity recognition and process mining on data obtained from sensorised workers and environments. Activity recognition lifts the abstraction level of sensor data to recognizable activities and process mining methods discover models of process executions. We classify the existing work, which largely neglects the possibility of applying process mining, and derive a taxonomy that identifies challenges and research gaps.
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A Taxonomy for Combining Activity Recognition and Process Discovery in Industrial Environments
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Full-text available
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Despite the increasing automation levels in an Industry 4.0 scenario, the tacit knowledge of highly skilled manufacturing workers remains of strategic importance. Retaining this knowledge by formally capturing it is a challenge for industrial organisations. This paper explores research on automatically capturing this knowledge by using methods from activity recognition and process mining on data obtained from sensorised workers and environments. Activity recognition lifts the abstraction level of sensor data to recognizable activities and process mining methods discover models of process executions. We classify the existing work, which largely neglects the possibility of applying process mining, and derive a taxonomy that identifies challenges and research gaps.
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