An Empirical Design Space Analysis of Doorway Tracking Systems for Real-World Environments

2017 ◽  
Vol 13 (4) ◽  
pp. 1-34 ◽  
Erin Griffiths ◽  
Avinash Kalyanaraman ◽  
Juhi Ranjan ◽  
Kamin Whitehouse
2017 ◽  
Vol 139 (11) ◽  
Wei Chen ◽  
Mark Fuge

To solve a design problem, sometimes it is necessary to identify the feasible design space. For design spaces with implicit constraints, sampling methods are usually used. These methods typically bound the design space; that is, limit the range of design variables. But bounds that are too small will fail to cover all possible designs, while bounds that are too large will waste sampling budget. This paper tries to solve the problem of efficiently discovering (possibly disconnected) feasible domains in an unbounded design space. We propose a data-driven adaptive sampling technique—ε-margin sampling, which learns the domain boundary of feasible designs and also expands our knowledge on the design space as available budget increases. This technique is data-efficient, in that it makes principled probabilistic trade-offs between refining existing domain boundaries versus expanding the design space. We demonstrate that this method can better identify feasible domains on standard test functions compared to both random and active sampling (via uncertainty sampling). However, a fundamental problem when applying adaptive sampling to real world designs is that designs often have high dimensionality and thus require (in the worst case) exponentially more samples per dimension. We show how coupling design manifolds with ε-margin sampling allows us to actively expand high-dimensional design spaces without incurring this exponential penalty. We demonstrate this on real-world examples of glassware and bottle design, where our method discovers designs that have different appearance and functionality from its initial design set.

2020 ◽  
Vol 10 (14) ◽  
pp. 4948
Marcel Neuhausen ◽  
Patrick Herbers ◽  
Markus König

Vision-based tracking systems enable the optimization of the productivity and safety management on construction sites by monitoring the workers’ movements. However, training and evaluation of such a system requires a vast amount of data. Sufficient datasets rarely exist for this purpose. We investigate the use of synthetic data to overcome this issue. Using 3D computer graphics software, we model virtual construction site scenarios. These are rendered for the use as a synthetic dataset which augments a self-recorded real world dataset. Our approach is verified by means of a tracking system. For this, we train a YOLOv3 detector identifying pedestrian workers. Kalman filtering is applied to the detections to track them over consecutive video frames. First, the detector’s performance is examined when using synthetic data of various environmental conditions for training. Second, we compare the evaluation results of our tracking system on real world and synthetic scenarios. With an increase of about 7.5 percentage points in mean average precision, our findings show that a synthetic extension is beneficial for otherwise small datasets. The similarity of synthetic and real world results allow for the conclusion that 3D scenes are an alternative to evaluate vision-based tracking systems on hazardous scenes without exposing workers to risks.

2015 ◽  
Vol 44 ◽  
pp. 125-134 ◽  
Jeremy Coffeen ◽  
Frederic Jacquelin ◽  
Richard Kepple ◽  
Rock Mendenhall ◽  
Michael Rodgers ◽  

2021 ◽  
Vol 227 ◽  
pp. 113599
Andrés Sebastián ◽  
Rubén Abbas ◽  
Manuel Valdés ◽  
Antonio Rovira

2020 ◽  
Vol 67 (8) ◽  
pp. 3102-3108
Hung-Li Chiang ◽  
Tzu-Chiang Chen ◽  
Ming-Yuan Song ◽  
Yu-Sheng Chen ◽  
Jung-Piao Chiu ◽  

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