Automatic Behavior Understanding in Crisis Response Control Rooms

IJsselmuiden, Joris; Grosselfinger, Ann-Kristin; Münch, David; Arens, Michael; Stiefelhagen, Rainer

doi:10.1007/978-3-642-34898-3_7

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Reducing the cognitive load and enabling immediate reaction to alarms in idle times are key requirements that have driven the development of the innovative control centre described in [61]. Comparable efforts on concrete applications have worked on ship bridges [66] and crises response control rooms [56].…”

Section: Building a Working Prototype: The Example Of Intelligent Conmentioning

confidence: 99%

Open Challenges in Modelling, Analysis and Synthesis of Human Behaviour in Human–Human and Human–Machine Interactions

et al. 2015

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Modeling, analysis and synthesis of behaviour are the subject of major efforts in computing science, especially when it comes to technologies that make sense of humanhuman and human-machine interactions. This article outlines some of the most important issues that still need to be addressed to ensure substantial progress in the field, namely 1) development and adoption of virtuous data collection and sharing practices, 2) shift of the focus of interest from individuals to dyads and groups, 3) endowment of artificial agents with internal representations of users and context, 4) modeling of cognitive and semantic processes underlying social behaviour, and 5) identification of application domains and strategies for moving from laboratory to the realworld products.

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Section: Building a Working Prototype: The Example Of Intelligent Conmentioning

confidence: 99%

Open Challenges in Modelling, Analysis and Synthesis of Human Behaviour in Human–Human and Human–Machine Interactions

et al. 2015

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“…These state attributes can be discriminative properties of the objects and the people, e.g., the object is still or moving, the person is walking or sitting. Typical conventional systems for ambient intelligence have been developed with the sophisticated sensing technologies [5] with various sensors embedded in objects and clothing of people in the environment. We have witnessed a significant progress of autonomous mobile robots in these years [4], [7].…”

Section: Introductionmentioning

confidence: 99%

Fast Adaptive Object Detection towards a Smart Environment by a Mobile Robot

Takano

Loshchilov

Meunier

et al. 2013

Lecture Notes in Computer Science

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Abstract. This paper proposes a novel method to detect objects by a mobile robot which adapts to an environment. Such a robot would help human designers of a smart environment to recognize objects in the environment with their attributes, which significantly facilitates his/her design. We first introduce Lifting Complex Wavelet Transform (LCWT) which plays an important role in this work. Since the LCWT has a set of controllable free parameters, we can design the LCWTs with various properties by tuning their parameters. In this paper we construct a set of LCWTs so that they can extract local features from an image by multi-scale. The extracted local features must be robust against several kinds of changes of the image such as shift, scale and rotation. Our method can design these LCWTs by selecting their parameters so that the mobile robot adapts to the environment. Applying the new set of LCWTs to the images captured by the mobile robot in the environment, a local feature database can be constructed. By using this database, we implement an object detection system based on LCWTs on the mobile robot. Effectiveness of our method is demonstrated by several test results using the mobile robot.

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