Crane Safety System with Monocular and Controlled Zoom Cameras

Vierling, Axel; Sutjaritvorakul, Tanittha; Berns, Karsten

doi:10.22260/isarc2018/0123

Cited by 8 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The load is often not in direct sight of the crane operator. Therefore surveillance with a camera mounted on the boom top is used to increase the safety [1]. Due to the birds-eye-view, vertical and horizontal translational robustness is a natural aspect the detection algorithm should fulfill.…”

Section: Introductionmentioning

confidence: 99%

Provable Translational Robustness For Object Detection With Convolutional Neural Networks

Vierling

James

Berns

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

Self Cite

View full text Add to dashboard Cite

In the following work object detection approaches with Convolutional Neural Networks (CNNs), which have provable characteristics regarding translational robustness, are proposed, evaluated in an application scenario, and compared to state of the art approaches. The provable characteristics are achieved by transferring theoretical results from wavelet theory and scattering networks to common CNNs used for classification. Therefore first a CNN is modeled as a scattering network. Needed parameters are estimated with data relevant for application scenarios. With the obtained information first the best feature extractor for a given application scenario is chosen. Afterward, the theory is extended to cover object detection networks. The proposed approaches are trained on simulated and real datasets and evaluated on real datasets.

show abstract

Section: Introductionmentioning

confidence: 99%

Provable Translational Robustness For Object Detection With Convolutional Neural Networks

Vierling

James

Berns

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Secondly, workers are detected in video images using Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) classifiers [13] and are then tracked over time [14]. More recent approaches employ Convolutional Neural Networks (CNNs) for both detection and tracking purposes [15,16]. However, the detection results of such approaches are in need of improvement.…”

Section: Introductionmentioning

confidence: 99%

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

2020

View full text Add to dashboard Cite

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.

show abstract