Comparison of semantic segmentation approaches for horizon/sky line detection

Ahmad, Touqeer; Campr, Pavel; Čadík, Martin; Bebis, George

doi:10.1109/ijcnn.2017.7966418

Cited by 15 publications

(20 citation statements)

References 33 publications

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“…However, this probabilistic approach is general enough to include horizon detection if needed. Semantic segmentation approach is evaluated by Ahmad et al (2017) on land-sky images under various weather and illumination conditions. Fully convolutional network performed the best on said images, but further post processing is required to improve the segmentation.…”

Section: Ann-based Methodsmentioning

confidence: 99%

An Overview on Horizon Detection Methods in Maritime Video Surveillance

2020

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The interest in video surveillance has been increasing in the fields of maritime industry in the past decade. Maritime transportation system is a vital part of the world’s economy and the extent of global ship traffic is increasing. This trend encourages the development of intelligent surveillance systems in the maritime zone. The development of intelligent surveillance systems includes sensor and data fusion, which incorporates multispectral and multisensory data to replace the traditional approach with radars only. Video cameras are widely used since they capture images of greater resolution than most sensor systems. Also, combined with video analytics they provide sensors with high capability, complex pattern recognition analytics, and multiple variables for the decision making process. In this paper, an overview of a small part of the system is presented – horizon detection.

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Section: Ann-based Methodsmentioning

confidence: 99%

An Overview on Horizon Detection Methods in Maritime Video Surveillance

2020

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“…A similar solution is investigated by Hung et al (2013), where a support vector machine is trained for classifying skyline and non-skyline edge segments. A comparison of four autonomous skyline segmentation techniques that use machine learning is reviewed by Ahmad et al (2017). The above-mentioned studies focus only skyline extraction, and their outcomes are hard to compare with the results of this paper.…”

Section: Related Workmentioning

confidence: 99%

A New Method of Improving the Azimuth in Mountainous Terrain by Skyline Matching

Nagy

2020

PFG

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Augmented reality (AR) applications have a serious problem with the accuracy of the azimuth angle provided by mobile devices. The fusion of the digital magnetic compass (DMC), accelerometer and gyroscope gives the translation and rotation of the observer in 3D space. However, the precision is not always appropriate since DMC is prone to interference when using it near metal objects or electric currents. The silhouette of ridges separates the sky from the terrain and forms the skyline or horizon line in a mountainous scenery. This salient feature can be used for orientation. With the camera of the device and a digital elevation model (DEM) the correct azimuth angle could be determined. This study proposes an effective method to adjust the azimuth by identifying the skyline from an image and matches it with the skyline of the DEM. This approach does not require manual interaction. The algorithm has also been validated in a real-world environment. ZusammenfassungEine neue Methode zur Verbesserung des Azimuts im Bergland auf Basis des Matchings der Horizontlinie. Augmented Reality (AR)-Anwendungen haben oft ein Problem mit der begrenzten Genauigkeit des Azimuts, das von mobilen Geräten geliefert wird. Die Verknüpfung eines digitalen Magnetkompasses, eines Beschleunigungsmessers und eines Vermessungskreisels liefern die Translation und Rotation eines Beobachters im 3D-Raum. Die Genauigkeit ist jedoch nicht immer brauchbar, weil ein Magnetkompass anfällig für Störungen ist, vor allem verursacht durch Metall und elektrische Ströme. Die Silhouette von Bergen bildet die Horizontlinie, die für eine Richtungsbestimmung (Orientierung) genutzt werden kann. Mit einer Kamera und einem digitalen Höhenmodell (DEM) konnte mit guter Genauigkeit das Azimut bestimmt werden. Die Untersuchung schlägt eine effektive Methode vor, um das Azimut eines Magnetkompasses durch Vergleich der Horizontlinie aus einem Bild mit der gerechneten Horizontlinie aus einem DEM zu bestimmen. Dieser Ansatz benötigt keinen manuellen Eingriff. Der Algorithmus wurde in realen Umgebungen validiert.

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“…But, It needs prior knowledge of sky region. To detect the horizon /sky line, to focus on visual geo-localization based on accurate detection of skyline, to develop algorithms for semantic segmentation [3] proposed classical feature learning method, patch-wise classifier training method, deep-learning network methods(FNN, SegNet) respectively for publicly available CH1 data set. But, learning techniques are more complex.…”

Section: Literature Surveymentioning

confidence: 99%

Efficient Horizon Line Detection using Clustering and Fast Marching Method

Sravanthi*,

Sarma

2020

IJITEE

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Video analysis of maritime scenarios typically includes detection of horizon line for reference. The horizon line is the imaginary line, which separates water and sky as well as water and land. The horizon line plays a major role in terms of demarcating the water region in the video frame for further analysis. Considerable research has been aimed at horizon line detection. Various approaches have been reported including (i) Canny based edge detection followed by Hough transform, (ii) machine learning combined with statistical methods. However, the Hough transform has several limitations, in terms excessive analysis time, deviation of estimated line from the actual horizon line, sensitivity to presence of floating objects on the horizon, error due to presence of large number of edges. Present paper describes an efficient method for detecting the horizon line for analysis videos obtained by cameras mounted on floating vessels such as unmanned surface vehicle in maritime and inland scenarios. The proposed method is based on K-means clustering followed by seed based region growing using Fast Marching Method. For detecting the horizon line, two clusters are used in water-sky region like in marine environment images whereas three or more clusters are used in water-land-sky region like in in-land rivers/lakes images. In most cases, the upper part of the frame belongs to sky region whereas lower part belongs to water region. After K means clustering, based on the selection of seed point in lower part of the frame, the water region is segmented using fast marching method from non water regions and hence the horizon line is detected. This proposed method performance is compared with edge detection followed by Hough transform for different datasets. Experimental results show that the proposed method detects efficient line without compromising the processing time. Index Terms -Unmanned surface vehicle (USV); Horizon line detection; Marine environment scenes, in-land rivers and lakes, K-means clustering.

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Comparison of semantic segmentation approaches for horizon/sky line detection

Cited by 15 publications

References 33 publications

An Overview on Horizon Detection Methods in Maritime Video Surveillance

An Overview on Horizon Detection Methods in Maritime Video Surveillance

A New Method of Improving the Azimuth in Mountainous Terrain by Skyline Matching

Efficient Horizon Line Detection using Clustering and Fast Marching Method

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