Constantinos Lalos

A dataset for workflow recognition in industrial scenes

In this paper we introduce the WR (Workflow Recognition) dataset. Recorded in the production line of a major automobile manufacturer, this dataset consists of sequences that depict workers executing industrial workflows. The heavy occlusions, outliers, the visually complicated background and the human-machinery interaction are among the factors that make this dataset a very challenging testbed for computer vision and image processing algorithms. We provide the original video sequences together with event labeling, as well as feature vectors extracted through our proposed scene representation methodology, and we refer to our results so far in workflow recognition using this dataset.

A Threefold Dataset for Activity and Workflow Recognition in Complex Industrial Environments

Unlike any previous effort, the Workflow Recognition (WR) large-scale dataset is a collection of video sequences from the real industrial manufacturing environment of a major automobile manufacturer.

Efficient tracking using a robust motion estimation technique

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.

A Cross Media Platform for Personalized Leisure & Entertainment: The POLYMNIA Approach

The POLYMNIA project aims to develop an intelligent cross-media platform for personalised leisure and entertainment in thematic parks or venues. The system allows the visitors to be the real protagonist in the venue. Towards this goal, POLYMNIA platform is equipped with innovative imaging technologies for real time detection, localisation and tracking of i human contenti, i.e., the human visitor within the recoding being made in real-time by the system. No constraints are imposed on the variation of the environment. New, content-based media representation and organisation schemes will be developed to provide scalable, efficient and user-oriented description of the i human contenti, enabling efficient retrieval, access, and delivery across heterogeneous media platforms. In addition, adaptive mechanisms are employed to update the system response to the current usersi information needs and preferences

An industrial video surveillance system for quality assurance of a manufactory assembly

In this paper, we present the research achievements of a computer vision system that surveys the behavior of workers in an industrial environment. The proposed system applies weakly supervised learning algorithms able to detect objects, then to extract the behaviors of workers with respect to the predefined assembly construction for a task and adopts adaptation mechanisms able to dynamically modified the performance of a system with respect to the environmental changes. The proposed architecture has been tested on real-life applications.

Hybrid tracking approach for assistive environments

Camera based supervision is a critical component for patient monitoring in assistive environments. However, visual tracking still remains one of the biggest challenges in the area computer vision although it has been extensively studied during the previous decades. It this paper we propose a hybrid Rao -- Blackwellzed particle filter that combines two efficient, well-known tracking techniques with an innovative color observation representation method in order to improve the overall tracking performance. This representation is combined with color and edge representation to obtain improved tracking efficiency. Furthermore, the global edge description template for the edge representation (histogram of oriented gradients) was obtained using a machine learning technique. Initial experiments show that the principle behind the proposed algorithm is sound, yielding good results and thus allowing its adoption as an initial stage for patient behavior recognition.

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.

Human tracking driven activity recognition in video streams

In this paper we describe a method to visually recognize activities from video streams driven by a robust tracking algorithm. The tracking algorithm uses object flow as a motion model that estimates the displacement and the direction of objects in imagery streams; as observation model, object flow is complemented by a generative prior based on the Gaussian Process Latent Variable Model for increased robustness to visual alterations of the tracked object (human). We then use tracking results as input to drive the recognition of activities by hidden Markov models-based classifiers. We evaluate the performance of the examined approach under real-life activity recognition scenarios in visually complex environments characterized by lighting changes, occlusions and outliers.

Identity management in a mobile service orientated grid

Composite applications consisting of multiple mobile services require intricate service management and security infrastructures. Grid based approaches to service management can be applied to provide such infrastructures to both support and secure users and services in these composite mobile applications. The Akogrimo project has applied WSRF based Service Oriented toolkits as the core element of a mobile Service Oriented Infrastructure (SOI). Security is described in relation to the domains into which the SOI is split — Network Provider, Service Provider and two types of Virtual Organization. This paper describes how the Akogrimo SOI has been secured by an approach based on the management of identities within it. The results of this have been demonstrated initially in an E-Health scenario that is one of the first examples of a truly mobile Service Orientated Architecture.