Detection Of People Stream Features Using Eigenvalue Maps

Large crowd video surveillance is important with respect to safety. Especially in the case of unexpected events it is beneficial to be able to detect certain features like bottlenecks as quick as possible. A number of methods have been proposed to find such occurrences but accuracy is still lacking. Our research expands on a previously presented method in order to improve the detection rate of important features. This project focusses only on bottlenecks. Eigenvalue maps derived from Jacobian matrices resulting from opical flow analysis are used to find bottlenecks in people streams. An accuracy of \textgreater 80\% was obtained using a varied but small dataset. The results indicate that using eigenvalue maps for feature detection are feasible and more reliable compared to earlier proposed similar methods

Robots guiding small groups: the effect of appearance change on the user experience

In this paper we present an exploratory user study in which a robot guided small groups of two to three people. We manipulated the appearance of the robot in terms of the position of a tablet providing information (facing the group that was guided or the walking direction) and the type of information displayed (eyes or route information). Our results indicate that users preferred eyes on a display that faced the walking direction and route information on a display that faced them. The study gave us strong indication to believe that people are not in favor of eyes looking at them during the guiding

Towards a COTS-Enabled Federated Cloud Architecture for Adaptive C2 in Coalition Tactical Operations: A Performance Analysis of Kubernetes

Nowadays, ever-increasing processing and storage resources are available at all echelons, from operations centers to tactical units. However, tactical-edge communications still suffer from scarce network resources such as limited bandwidth, intermittent connectivity, and variable latency. In addition, modern military missions typically involve coalition operations, where heterogeneous mission partners (even belonging to different nations) cooperate in the field. As a result, the distribution of mission-critical information is more complicated than ever. On the one hand, the dynamic nature of the tactical environment frequently disrupts communications. On the other hand, individual resource-sharing policies prevent mission partners from taking full advantage of the available resources in situ. The NATO IST-168 RTG has been exploring commercial-off-the-shelf orchestration technologies for implementing a federated cloud architecture that enables adaptive information processing and dissemination while living within the constraints of the tactical domain. This paper is a follow-up study that assesses the behaviour of Kubernetes under the disadvantaged network conditions characterizing tactical edge networks

Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics

Logistics planning is a complex optimization problem involving multiple decision makers. Automated scheduling systems offer support to human planners; however state-of-the-art approaches often employ a centralized control paradigm. While these approaches have shown great value, their application is hindered in dynamic settings with no central authority. Motivated by real-world scenarios, we present a decentralized approach to collaborative multi-agent scheduling by casting the problem as a Distributed Constraint Optimization Problem (DCOP). Our model-based heuristic approach uses message passing with a novel pruning technique to allow agents to cooperate on mutual agreement, leading to a near-optimal solution while offering low computational costs and flexibility in case of disruptions. Performance is evaluated in three real-world field trials with a logistics carrier and compared against a centralized model-free Deep Q-Network (DQN)-based Reinforcement Learning (RL) approach, a Mixed-Integer Linear Programming (MILP)-based solver, and both human and heuristic baselines. The results demonstrate that it is feasible to have virtual agents make autonomous decisions using our DCOP method, leading to an efficient distributed solution. To facilitate further research in Self-Organizing Logistics (SOL), we provide a novel real-life dataset.</p

Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics

Logistics planning is a complex optimization problem involving multiple decision makers. Automated scheduling systems offer support to human planners; however state-of-the-art approaches often employ a centralized control paradigm. While these approaches have shown great value, their application is hindered in dynamic settings with no central authority. Motivated by real-world scenarios, we present a decentralized approach to collaborative multi-agent scheduling by casting the problem as a Distributed Constraint Optimization Problem (DCOP). Our model-based heuristic approach uses message passing with a novel pruning technique to allow agents to cooperate on mutual agreement, leading to a near-optimal solution while offering low computational costs and flexibility in case of disruptions. Performance is evaluated in three real-world field trials with a logistics carrier and compared against a centralized model-free Deep Q-Network (DQN)-based Reinforcement Learning (RL) approach, a Mixed-Integer Linear Programming (MILP)-based solver, and both human and heuristic baselines. The results demonstrate that it is feasible to have virtual agents make autonomous decisions using our DCOP method, leading to an efficient distributed solution. To facilitate further research in Self-Organizing Logistics (SOL), we provide a novel real-life dataset.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Algorithmic