Airborne Vehicle Detection in Dense Urban Areas Using HoG Features and Disparity Maps

Tuermer, Sebastian; Kurz, Franz; Reinartz, Peter; Stilla, Uwe

doi:10.1109/jstars.2013.2242846

Cited by 154 publications

(82 citation statements)

References 42 publications

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“…In a similar manner, Eikvil et al [23] proposed a detection approach that separates regions with high probability to contain cars, followed by two stages of object classification exploiting multi-spectral images, geometric properties and road networks. Following the same idea, Tuermer et al [24] proposed filtering areas with very low probability to contain cars using road databases, disparity maps and a pre-processing stage. Whilst using road maps provides better performance in terms of detection and false-positive rates, it requires accurate road maps to be known a priori and a precise map-projection method or the use of a global positioning system (GPS).…”

Section: Related Workmentioning

confidence: 99%

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Car Detection in Aerial Images of Dense Urban Areas

Elmikaty

Stathaki

2018

IEEE Trans. Aerosp. Electron. Syst.

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Abstract-With the ever-increasing demand in the analysis and understanding of aerial images in order to remotely recognise targets, this paper introduces a robust system for the detection and localisation of cars in images captured by air vehicles and satellites. The system adopts a sliding-window approach. It compromises a window-evaluation and a window-classification sub-systems. The performance of the proposed framework was evaluated on the Vaihingen dataset. Results demonstrate its superiority to the state of the art.

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Section: Related Workmentioning

confidence: 99%

“…In urban scenes, cars are more likely to be found in road and parking areas [20]- [24]. The extraction of these areas prior to the detection process would eliminate a large number of false positives, which are produced as a result of the visual similarity among cars and other objects.…”

Section: A Extraction Of Regions Of Interestmentioning

confidence: 99%

Car Detection in Aerial Images of Dense Urban Areas

Elmikaty

Stathaki

2018

IEEE Trans. Aerosp. Electron. Syst.

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“…In order to restrict search areas to regions that are more likely to contain cars, road maps can be exploited [5]. Whilst this results in high precision rates, it is based on the prior knowledge of accurate road maps and on the use of a precise map-projection method.…”

Section: Related Workmentioning

confidence: 99%

Detection of cars in complex urban areas

Elmikaty

Stathaki

2017

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)

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Detection of cars in airborne images of typical urban areas has various applications in several domains, such as surveillance, military and remote sensing. It is a tremendously-challenging problem, mainly because of the significant inter-class similarity among various objects in urban environments. In this paper, a novel framework is introduced that adopts a sliding-window approach and it depicts, in a novel way, the local distribution of gradients, colours and texture. A linear support vector machine classifier is used to differentiate between descriptors that belong to cars and descriptors that belong to other objects in a hyperspace of 3838 dimensions. Descriptors are computed over a newlyproposed adaptive distribution of cells that enables the use of various rotation-variant image descriptors. The proposed framework has been evaluated on the Vaihingen dataset and results corroborate its superiority as it achieves a higher precision for a given recall than the state of the art.

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“…Similarly, the producer's accuracy (column values) was computed considering the agreement of a particular class to the summation of that column. In many cases, according to Zhan et al [105] and Tuermer et al [106], the user's accuracy represents a measure of correctness (Equation (9)), and the producer's accuracy represents as a measure of completeness (Equation (10)).…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

Al‐Rawabdeh

Moussa

et al. 2016

Remote Sensing

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Landslides often cause economic losses, property damage, and loss of lives. Monitoring landslides using high spatial and temporal resolution imagery and the ability to quickly identify landslide regions are the basis for emergency disaster management. This study presents a comprehensive system that uses unmanned aerial vehicles (UAVs) and Semi-Global dense Matching (SGM) techniques to identify and extract landslide scarp data. The selected study area is located along a major highway in a mountainous region in Jordan, and contains creeping landslides induced by heavy rainfall. Field observations across the slope body and a deformation analysis along the highway and existing gabions indicate that the slope is active and that scarp features across the slope will continue to open and develop new tension crack features, leading to the downward movement of rocks. The identification of landslide scarps in this study was performed via a dense 3D point cloud of topographic information generated from high-resolution images captured using a low-cost UAV and a target-based camera calibration procedure for a low-cost large-field-of-view camera. An automated approach was used to accurately detect and extract the landslide head scarps based on geomorphological factors: the ratio of normalized Eigenvalues (i.e., λ1/λ2 ě λ3) derived using principal component analysis, topographic surface roughness index values, and local-neighborhood slope measurements from the 3D image-based point cloud. Validation of the results was performed using root mean square error analysis and a confusion (error) matrix between manually digitized landslide scarps and the automated approaches. The experimental results using the fully automated 3D point-based analysis algorithms show that these approaches can effectively distinguish landslide scarps. The proposed algorithms can accurately identify and extract landslide scarps with centimeter-scale accuracy. In addition, the combination of UAV-based imagery, 3D scene reconstruction, and landslide scarp recognition/extraction algorithms can provide flexible and effective tool for monitoring landslide scarps and is acceptable for landslide mapping purposes.

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Airborne Vehicle Detection in Dense Urban Areas Using HoG Features and Disparity Maps

Cited by 154 publications

References 42 publications

Car Detection in Aerial Images of Dense Urban Areas

Car Detection in Aerial Images of Dense Urban Areas

Detection of cars in complex urban areas

Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

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