Moving Objects Detection and Tracking Framework for UAV-based Surveillance

Ibrahim, Aryo Wiman Nur; Ching, Pang Wee; Seet, Gerald; Lau, Wui-Man; Czajewski, Witold

doi:10.1109/psivt.2010.83

Cited by 50 publications

(15 citation statements)

References 11 publications

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“…[20][21][22] Segmentation techniques can be based on thresholding, 23,24 morphological operations, 25 edge detection, 15,26 or superpixels 27,28 in combination with connected component labeling while machine learning approaches use trained classifiers in a sliding-window framework [29][30][31] often only applied to independently moving image regions. [32][33][34] To further improve those methods, several approaches exist for spatial information fusion 15,26,31,35,36 and consideration of context knowledge, such as street networks or tracking statistics. 18,25,32,33,[37][38][39] Temporal information fusion, however, is often introduced by using single or multiple object tracking that is based on initial detections.…”

Section: Related Workmentioning

confidence: 99%

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

2017

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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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Section: Related Workmentioning

confidence: 99%

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

2017

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“…This system runs fast but it cannot solve the complex scaling scenarios. Ibrahim et al [ 17 ] proposed the MODAT framework. Instead of Harris corner, they adopted SIFT (Scale-invariant feature transform) [ 18 ] features to fulfill the image matching.…”

Section: Related Workmentioning

confidence: 99%

Multi-Model Estimation Based Moving Object Detection for Aerial Video

Zhang

Tong

Yang

et al. 2015

Sensors

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With the wide development of UAV (Unmanned Aerial Vehicle) technology, moving target detection for aerial video has become a popular research topic in the computer field. Most of the existing methods are under the registration-detection framework and can only deal with simple background scenes. They tend to go wrong in the complex multi background scenarios, such as viaducts, buildings and trees. In this paper, we break through the single background constraint and perceive the complex scene accurately by automatic estimation of multiple background models. First, we segment the scene into several color blocks and estimate the dense optical flow. Then, we calculate an affine transformation model for each block with large area and merge the consistent models. Finally, we calculate subordinate degree to multi-background models pixel to pixel for all small area blocks. Moving objects are segmented by means of energy optimization method solved via Graph Cuts. The extensive experimental results on public aerial videos show that, due to multi background models estimation, analyzing each pixel’s subordinate relationship to multi models by energy minimization, our method can effectively remove buildings, trees and other false alarms and detect moving objects correctly.

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“…In this study, the random sample consensus (RANSAC) algorithm, which uses homography as the geometric constraint model, is applied to remove the pair of mismatched keypoints. Homography is applied to the two images in case of translation, 3D rotation (roll, pitch, and yaw), and zoom transformation [20]. When translation, rotation, and zoom transformation occur in both the visible image and the database image acquired through the infrared camera, homography becomes the most suitable transformation for the geometric constraint model.…”

Section: Matching Refinementmentioning

confidence: 99%

Automatic 3D Thermal Modeling Using Thermal Data Obtained from Unknown Viewpoints

Lee

Son

Kim

2015

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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There has been an increasing need for diagnostic methods to detect energy leakage in order to reduce energy consumption of buildings. Currently, infrared thermography is used as a preliminary investigation tool because it does not cause physical damage during the exploratory investigation. In practice, diagnosis of the building using infrared thermography requires not only drawings of the building but also the properties of materials and information about the joints and junctions of the building components. However, often, accurate building drawings are not available for existing buildings, and this makes diagnosis of the building using infrared thermography difficult. This study aims to propose a method to automatically map infrared thermographs acquired during periodic diagnosis of the building to its as-built data without fixing the relative position and direction of the infrared camera and a sensor that is used to acquire the as-built 3D point cloud. The preliminary experimental result shows that the proposed method can automatically map an infrared thermograph to an as-built 3D point cloud acquired from different positions and at different times.

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Moving Objects Detection and Tracking Framework for UAV-based Surveillance

Cited by 50 publications

References 11 publications

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

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

Multi-Model Estimation Based Moving Object Detection for Aerial Video

Automatic 3D Thermal Modeling Using Thermal Data Obtained from Unknown Viewpoints

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