A simulated car-park environment for the evaluation of video-based on-site parking guidance systems

Tschentscher, Marc; Prus, Ben; Horn, Daniela

doi:10.1109/ivs.2017.7995933

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…To analyse the impact of rain, sun, and fog, Tschentscher et al (2017) implemented a new simulation system using Unreal Engine 4 to enhance the efficiency of the traditional parking system. The traditional parking system was based on an embedded system without any use of smart cameras.…”

Section: Assessing Environmental Impacts On Iot Imagesmentioning

confidence: 99%

“…For most of the applications, real datasets (covering all environmental impacts such as rain, fog, shadow, wind, darkness, and camera impacts such as lens blurriness, dirtiness, and barrel distortion) are not available. There exists a model that simulates the camera to generate the data for training traffic parking guidance systems considering the lens blur and lens distortion camera impacts (Tschentscher et al 2017). The main loophole of this simulation model is that this uses only lens blur and lens distortion camera impacts.…”

Section: Further Research Challengesmentioning

confidence: 99%

“…The advancement of DL techniques for the analysis and reduction of environmental and camera impacts require addressing the problem of the unavailability of the dataset mentioned before in this research challenge. Therefore, similar to the simulation model developed in Tschentscher et al (2017), there is a need for the development of a simulation model which is capable of generating data that closely represents various real-world environmental deployment scenarios. The development of such a simulation model also demands a technique to validate the simulated data against their real-world counterparts.…”

Section: Further Research Challengesmentioning

confidence: 99%

“…The performance analysis of existing DL-based distortion reduction techniques for all possible environmental and camera distortion impacts on the IoT images needs to be advanced further. For example,(Tschentscher et al 2017) covers only the impacts of fog, sun, rain, and snow but not other environmental impact factors such as wind, shadow, and darkness. Likewise, the approach presented in Kazerouni et al (2019) emphasises only wind and rain.…”

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confidence: 99%

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Deep learning: survey of environmental and camera impacts on internet of things images

et al. 2023

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Internet of Things (IoT) images are captivating growing attention because of their wide range of applications which requires visual analysis to drive automation. However, IoT images are predominantly captured from outdoor environments and thus are inherently impacted by the camera and environmental parameters which can adversely affect corresponding applications. Deep Learning (DL) has been widely adopted in the field of image processing and computer vision and can reduce the impact of these parameters on IoT images. Albeit, there are many DL-based techniques available in the current literature for analyzing and reducing the environmental and camera impacts on IoT images. However, to the best of our knowledge, no survey paper presents state-of-the-art DL-based approaches for this purpose. Motivated by this, for the first time, we present a Systematic Literature Review (SLR) of existing DL techniques available for analyzing and reducing environmental and camera lens impacts on IoT images. As part of this SLR, firstly, we reiterate and highlight the significance of IoT images in their respective applications. Secondly, we describe the DL techniques employed for assessing the environmental and camera lens distortion impacts on IoT images. Thirdly, we illustrate how DL can be effective in reducing the impact of environmental and camera lens distortion in IoT images. Finally, along with the critical reflection on the advantages and limitations of the techniques, we also present ways to address the research challenges of existing techniques and identify some further researches to advance the relevant research areas.

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Section: Assessing Environmental Impacts On Iot Imagesmentioning

confidence: 99%

Section: Further Research Challengesmentioning

confidence: 99%

Section: Further Research Challengesmentioning

confidence: 99%

mentioning

confidence: 99%

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Deep learning: survey of environmental and camera impacts on internet of things images

et al. 2023

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“…Furthermore, synthetic datasets can be augmented by creating variations of lighting or environmental conditions like rain or fog. Tschentscher et al [24] created a synthetic dataset of a car park in Unreal Engine 4 where camera angles and weather conditions can be changed at will. Through the use of this dataset, Horn and Houben [25] improved a k-NN based parking space detection algorithm by six percentage points while simultaneously reducing the time for generating the dataset.…”

Section: Introductionmentioning

confidence: 99%

Using Synthetic Data to Improve and Evaluate the Tracking Performance of Construction Workers on Site

2020

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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.

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Synthetic Data for Evaluating the Visual Tracking of Construction Workers

Neuhausen¹,

Herbers²,

König³

2020

Construction Research Congress 2020

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A simulated car-park environment for the evaluation of video-based on-site parking guidance systems

Cited by 6 publications

References 7 publications

Deep learning: survey of environmental and camera impacts on internet of things images

Deep learning: survey of environmental and camera impacts on internet of things images

Using Synthetic Data to Improve and Evaluate the Tracking Performance of Construction Workers on Site

Synthetic Data for Evaluating the Visual Tracking of Construction Workers

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