Object-based detection of vehicles using combined optical and elevation data

Schilling, Hendrik; Bulatov, Dimitri; Middelmann, Wolfgang

doi:10.1016/j.isprsjprs.2017.11.023

Cited by 14 publications

(5 citation statements)

References 57 publications

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“…Automatic measurements of traffic parameters can provide comprehensive information on how delays occur, and which routes could be suitable alternative options [20]. 3D information of remote sensing data can cover large areas and can provide valuable input to improve automatic vehicle detection and classification, identify obstacles on access routes, as well as estimate the number of people on site [21]. The added value of 3D change information is to be examined in more detail in the context of this work.…”

Section: D Data From Optical Remote Sensing For Crisis and Event Manmentioning

confidence: 99%

2D vs. 3D Change Detection Using Aerial Imagery to Support Crisis Management of Large-Scale Events

et al. 2018

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Large-scale events represent a special challenge for crisis management. To ensure that participants can enjoy an event safely and carefree, it must be comprehensively prepared and attentively monitored. Remote sensing can provide valuable information to identify potential risks and take appropriate measures in order to prevent a disaster, or initiate emergency aid measures as quickly as possible in the event of an emergency. Especially, three-dimensional (3D) information that is derived using photogrammetry can be used to analyze the terrain and map existing structures that are set up at short notice. Using aerial imagery acquired during a German music festival in 2016 and the celebration of the German Protestant Church Assembly of 2017, the authors compare two-dimensional (2D) and novel fusion-based 3D change detection methods, and discuss their suitability for supporting large-scale events during the relevant phases of crisis management. This study serves to find out what added value the use of 3D change information can provide for on-site crisis management. Based on the results, an operational, fully automatic processor for crisis management operations and corresponding products for end users can be developed.

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Section: D Data From Optical Remote Sensing For Crisis and Event Manmentioning

confidence: 99%

2D vs. 3D Change Detection Using Aerial Imagery to Support Crisis Management of Large-Scale Events

et al. 2018

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“…During the past decades, many methods were studied for object detection of HRRSI. In general, these methods can be mainly divided into four categories [3]: Template matching-based object detection [4][5][6], Knowledge-based object detection [7][8][9], Object-based object detection [10][11][12], and Machine learning-based object detection [13][14][15]. The similarity measure is utilized to find the best matches of templates generated manually or from the labeled instances in the template matching-based…”

Section: Introductionmentioning

confidence: 99%

Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network

Zhu

et al. 2019

Remote Sensing

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In this paper, the problem of multi-scale geospatial object detection in High Resolution Remote Sensing Images (HRRSI) is tackled. The different flight heights, shooting angles and sizes of geographic objects in the HRRSI lead to large scale variance in geographic objects. The inappropriate anchor size to propose the objects and the indiscriminative ability of features for describing the objects are the main causes of missing detection and false detection in multi-scale geographic object detection. To address these challenges, we propose a class-specific anchor based and context-guided multi-class object detection method with a convolutional neural network (CNN), which can be divided into two parts: a class-specific anchor based region proposal network (RPN) and a discriminative feature with a context information classification network. A class-specific anchor block providing better initial values for RPN is proposed to generate the anchor of the most suitable scale for each category in order to increase the recall ratio. Meanwhile, we proposed to incorporate the context information into the original convolutional feature to improve the discriminative ability of the features and increase classification accuracy. Considering the quality of samples for classification, the soft filter is proposed to select effective boxes to improve the diversity of the samples for the classifier and avoid missing or false detection to some extent. We also introduced the focal loss in order to improve the classifier in classifying the hard samples. The proposed method is tested on a benchmark dataset of ten classes to prove the superiority. The proposed method outperforms some state-of-the-art methods with a mean average precision (mAP) of 90.4% and better detects the multi-scale objects, especially when objects show a minor shape change.

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“…The systems have been applied in many applications, such as automatic-car systems [3], traffic surveillance [4], vehicle counting [5], and an application to find a car park automatically [6]. Computer vision-based [7] and sensor-based systems [8] are the most commonly used methods. The computer vision-based methods make the systems more robust than sensor systems in understanding and perceiving information [8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Selection in Vehicle Detection Through the Selection of Dominant Patterns of Histograms of Oriented Gradients (DPHOG)

2019

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This paper proposes a novel method that addresses the selection of the dominant patterns of the histograms of oriented gradients (DPHOGs) in vehicle detection. HOG features lead to an expensive classification with high misclassification rates since HOG generates a long vector containing both redundant and ambiguous features (similarities between the vehicle and non-vehicle images). Several modifications of HOG were proposed to resolve these issues such as the vertical histograms of oriented gradient and one that includes position and intensity with HOG; however, these methods still contain some ambiguous features. A feature selection method can exclude these ambiguous features, allowing for better classification rates and a reduction in classification times. The proposed method uses the ideal vectors of the vehicle and non-vehicles images for selecting features in dominant patterns. The segments indicating the differences between the vehicle and non-vehicle classes are the dominant patterns, in which the length of the feature vector is shortened. We performed DPHOG on three standard datasets, in which the kernel extreme learning machine, the support vector machine, K-nearest neighbor, random forest, and deep neural network were used as classifiers. We then compared the performance of the DPHOG with eight well-known feature selection methods and three existing feature extraction methods for vehicle detection. In evaluations with each comparative method concerning the accuracy, true positive, false positive, and F1-score, the DPHOG presented the highest performances with less running time in each dataset.

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Object-based detection of vehicles using combined optical and elevation data

Cited by 14 publications

References 57 publications

2D vs. 3D Change Detection Using Aerial Imagery to Support Crisis Management of Large-Scale Events

2D vs. 3D Change Detection Using Aerial Imagery to Support Crisis Management of Large-Scale Events

Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network

A Novel Feature Selection in Vehicle Detection Through the Selection of Dominant Patterns of Histograms of Oriented Gradients (DPHOG)

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