Evaluation of object segmentation to improve moving vehicle detection in aerial videos

Teutsch, Michael; Krüger, Wolfgang; Beyerer, Jürgen

doi:10.1109/avss.2014.6918679

Cited by 17 publications

(18 citation statements)

References 21 publications

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“…For both SS and SP the extraordinarily tiny or large region proposals are considered impossible for vehicles in satellite videos and removed by post-processing. In addition to these well-known region proposals techniques, two approaches for aerial object detection are also included for comparison, which are Maximally Stable Extremal Regions (MSER) [33] or Top-hat-Otsu [34].…”

Section: Comparison Of Region Proposal Approachesmentioning

confidence: 99%

“…For common computer vision tasks, generating region proposals are commonly guided by the object saliency, such as the edges [22][23][24], or based on superpixels [25][26][27][28][29] or segmentation masks [30,31]. In aerial videos, the coherent regions extracted by Maximally Stable Extremal Regions (MSER) [32,33] or Top-hat-Otsu [34] are also adopted for region proposal generation. Due to the weak contrast between targets and background in satellite videos, saliency-based approaches result in degraded region proposal performance -either generating too many region proposals or producing a low target recall rate.…”

Section: Introductionmentioning

confidence: 99%

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Local Region Proposing for Frame-Based Vehicle Detection in Satellite Videos

Zhang

Jia

2019

Remote Sensing

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Current new developments in remote sensing imagery enable satellites to capture videos from space. These satellite videos record the motion of vehicles over a vast territory, offering significant advantages in traffic monitoring systems over ground-based systems. However, detecting vehicles in satellite videos are challenged by the low spatial resolution and the low contrast in each video frame. The vehicles in these videos are small, and most of them are blurred into their background regions. While region proposals are often generated for efficient target detection, they have limited performance on satellite videos. To meet this challenge, we propose a Local Region Proposing approach (LRP) with three steps in this study. A video frame is segmented into semantic regions first and possible targets are then detected in these coarse scale regions. A discrete Histogram Mixture Model (HistMM) is proposed in the third step to narrow down the region proposals by quantifying their likelihoods towards the target category, where the training is conducted on positive samples only. Experiment results demonstrate that LRP generates region proposals with improved target recall rates. When a slim Fast-RCNN detector is applied, LRP achieves better detection performance over the state-of-the-art approaches tested.

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Section: Comparison Of Region Proposal Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Local Region Proposing for Frame-Based Vehicle Detection in Satellite Videos

Zhang

Jia

2019

Remote Sensing

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“…A popular approach used by several authors in the literature [ 1 , 4 , 10 , 29 , 39 , 40 ] is thermal image processing. Some approaches use only thermal images, ignoring RGB images, while others use thermal images only in the detection process.…”

Section: Related Workmentioning

confidence: 99%

Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV

Oliveira

Wehrmeister

2018

Sensors

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The use of Unmanned Aerial Vehicles (UAV) has been increasing over the last few years in many sorts of applications due mainly to the decreasing cost of this technology. One can see the use of the UAV in several civilian applications such as surveillance and search and rescue. Automatic detection of pedestrians in aerial images is a challenging task. The computing vision system must deal with many sources of variability in the aerial images captured with the UAV, e.g., low-resolution images of pedestrians, images captured at distinct angles due to the degrees of freedom that a UAV can move, the camera platform possibly experiencing some instability while the UAV flies, among others. In this work, we created and evaluated different implementations of Pattern Recognition Systems (PRS) aiming at the automatic detection of pedestrians in aerial images captured with multirotor UAV. The main goal is to assess the feasibility and suitability of distinct PRS implementations running on top of low-cost computing platforms, e.g., single-board computers such as the Raspberry Pi or regular laptops without a GPU. For that, we used four machine learning techniques in the feature extraction and classification steps, namely Haar cascade, LBP cascade, HOG + SVM and Convolutional Neural Networks (CNN). In order to improve the system performance (especially the processing time) and also to decrease the rate of false alarms, we applied the Saliency Map (SM) and Thermal Image Processing (TIP) within the segmentation and detection steps of the PRS. The classification results show the CNN to be the best technique with 99.7% accuracy, followed by HOG + SVM with 92.3%. In situations of partial occlusion, the CNN showed 71.1% sensitivity, which can be considered a good result in comparison with the current state-of-the-art, since part of the original image data is missing. As demonstrated in the experiments, by combining TIP with CNN, the PRS can process more than two frames per second (fps), whereas the PRS that combines TIP with HOG + SVM was able to process 100 fps. It is important to mention that our experiments show that a trade-off analysis must be performed during the design of a pedestrian detection PRS. The faster implementations lead to a decrease in the PRS accuracy. For instance, by using HOG + SVM with TIP, the PRS presented the best performance results, but the obtained accuracy was 35 percentage points lower than the CNN. The obtained results indicate that the best detection technique (i.e., the CNN) requires more computational resources to decrease the PRS computation time. Therefore, this work shows and discusses the pros/cons of each technique and trade-off situations, and hence, one can use such an analysis to improve and tailor the design of a PRS to detect pedestrians in aerial images.

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“…The first approach is motivated by appearance-based object detection using machine learning 34 and the second approach is based on object segmentation by the clustering of edge pixels. 49 The result of both methods is bounding boxes that surround the moving objects. For simplicity and to be consistent with existing literature, we call those bounding boxes "detections" even if they are the result of object segmentation.…”

Section: Object Detection and Segmentationmentioning

confidence: 99%

“…Split detections can occur for weakly textured vehicles, and merged detections appear due to the similar motion direction and velocity in dense urban traffic. 49 Thus, the motion clusters are considered as initial object hypotheses and define a search space. Before object detection and segmentation are applied, the motion clusters are spatially extended in the motion direction to fully contain objects that are detected partially by independent motion detection.…”

Section: Object Detection and Segmentationmentioning

confidence: 99%

Moving object detection in top-view aerial videos improved by image stacking

2017

Self Cite

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Abstract. Image stacking is a well-known method that is used to improve the quality of images in video data. A set of consecutive images is aligned by applying image registration and warping. In the resulting image stack, each pixel has redundant information about its intensity value. This redundant information can be used to suppress image noise, resharpen blurry images, or even enhance the spatial image resolution as done in superresolution. Small moving objects in the videos usually get blurred or distorted by image stacking and thus need to be handled explicitly. We use image stacking in an innovative way: image registration is applied to small moving objects only, and image warping blurs the stationary background that surrounds the moving objects. Our video data are coming from a small fixed-wing unmanned aerial vehicle (UAV) that acquires top-view gray-value images of urban scenes. Moving objects are mainly cars but also other vehicles such as motorcycles. The resulting images, after applying our proposed image stacking approach, are used to improve baseline algorithms for vehicle detection and segmentation. We improve precision and recall by up to 0.011, which corresponds to a reduction of the number of false positive and false negative detections by more than 3 per second. Furthermore, we show how our proposed image stacking approach can be implemented efficiently. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Evaluation of object segmentation to improve moving vehicle detection in aerial videos

Cited by 17 publications

References 21 publications

Local Region Proposing for Frame-Based Vehicle Detection in Satellite Videos

Local Region Proposing for Frame-Based Vehicle Detection in Satellite Videos

Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV

Moving object detection in top-view aerial videos improved by image stacking

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