Videodetektion im Straßenverkehr

Döge, Klaus-Peter

doi:10.1524/9783486720075

Cited by 3 publications

(4 citation statements)

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“…and n here is the area of the object, which is represented by the number of pixels that belong to the object, x i and y i are the coordinates of pixels that belong to the object [28,37].…”

Section: Centroid Calculationmentioning

confidence: 99%

Implementing a Hybrid Method for Shack–Hartmann Wavefront Spots Labeling on FPGA

Abdullah,

Brady,

Heinig

et al. 2024

Electronics

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This paper presents a real-time implementation of a hybrid connected component labeling method for processing the Shack–Hartmann wavefront sensor’s images for an adaptive optics (AO) system. The output image of a wavefront sensor is an image of spots. During the sensor’s operation, it can happen that highly distorted wavefronts (WF) may cause the spots to shift outside of their sub-aperture, which may lead to the reduction of the AO system performance. This article explains the benefits of high-performance computing and parallel processing of a field programmable gate array (FPGA). The objective is to calculate the centroids of these spots. A hybrid labeling method was investigated to fulfill this purpose. First, this method was implemented using a forward and backward scan with a respective mask for each scan. Additionally, a relabeling process is applied after labeling each line, and it is carried out in both directions. After labeling, several processing units were implemented in parallel to calculate centroids. Each unit is responsible for calculating the centroid of one label. The system runs in real time with a latency of one frame, which means the output image is a fusion of a current frame and the centroids of the previous frame. Forward and backward labeling requires a large amount of memory, which is the reason for limiting the investigation to forward labeling only. The forward labeling was successfully implemented, and the centroids were detected under minimum spot distortion conditions. This forward labeling implementation also runs in real time with significant latency reduction to calculate the centroids, which leads to minimizing the overall AO system latency, enabling faster computation and correction in addition to reducing the memory usage to 1% when compared to the forward and backward labeling usage of 81% as an advantage for the hardware implementation.

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“…and n here is the area of the object, which is represented by the number of pixels that belong to the object, x i and y i are the coordinates of pixels that belong to the object [28,37].…”

Section: Centroid Calculationmentioning

confidence: 99%

Implementing a Hybrid Method for Shack–Hartmann Wavefront Spots Labeling on FPGA

Abdullah,

Brady,

Heinig

et al. 2024

Electronics

Self Cite

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“…During the determination of the background image, the following problems have to be considered [9]:  Daytime images without vehicles on average used roads are rare.…”

Section: Background Estimationmentioning

confidence: 99%

“…Thus, the background image must be estimated and continuously adapted to the changing environmental conditions such as changes in brightness. An established method of background estimation is the calculation of an arithmetic average image [9]. To reduce the storage requirements, a recursive averaging is possible [10].…”

Section: Background Estimationmentioning

confidence: 99%

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Automated Traffic Analysis in Aerial Images

Hößler

Landgraf

2014

International Journal on Smart Sensing and Intelligent Systems

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In this work an automated traffic analysis in aerial image sequences recorded by an unmanned aerial vehicle (UAV) has been developed, which provides the trajectories of the vehicles especially during overtaking. Methods of photogrammetry and image processing are used to detect the vehicles and to determine and track their positions. Camera calibration is necessary in order to transform image coordinates into world coordinates.

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