Multi-object Tracking in Aerial Image Sequences using Aerial Tracking Learning and Detection Algorithm

<p>Vison based tracking in aerial images has its own significance in the areas of both civil and defense applications. A novel algorithm called aerial tracking learning detection which works on the basis of the popular tracking learning detection algorithm to effectively track single and multiple objects in aerial images is proposed in this study. Tracking learning detection (TLD) considers both appearance and motion features for tracking. It can handle occlusion to certain extent, and can work well on long duration video sequences. However, when objects are tracked in aerial images taken from platforms like unmanned air vehicle, the problems of frequent pose change, scale and illumination variations arise adding to low resolution, noise and jitter introduced by motion of the camera. The proposed algorithm incorporates compensation for the camera movement, algorithmic modifications in combining appearance and motion cues for detection and tracking of multiple objects and enhancements in the form of inter object distance measure for improved performance of the tracker when there are many identical objects in proximity. This algorithm has been tested on a large number of aerial sequences including benchmark videos, TLD dataset and many classified unmanned air vehicle sequences and has shown better performance in comparison to TLD.</p><p> </p>

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An Automatic Detection Algorithm of Bridges above Water in Aerial Images

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.610-613.3670 ◽

2012 ◽

Vol 610-613 ◽

pp. 3670-3675

Author(s):

Yin Wen Dong ◽

Bing Cheng Yuan ◽

Jing Xin An ◽

Zhao Ming Shi ◽

Ming Lei Zhu

Keyword(s):

Automatic Detection ◽

Detection Algorithm ◽

Water Area ◽

Binary Image ◽

Aerial Images ◽

Aerial Image ◽

Mean Square ◽

Automatic Detection Algorithm

An automatic detection algorithm of bridges above water in aerial images is proposed. Firstly, aerial image is binarized based on gradient mean square variance, and binary image is denoised based on pixel density. Then connective areas are labeled in the binary image based on pixels to extract water area. Finally, bridge area is detected based on gray information. Experiments show this algorithm is effective in automatic detection of bridges above water in aerial images.

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Computer Aided Design, Analysis and Prototype Development of 2-Axis Stabilized Aerial Image Capturing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.225.310 ◽

2012 ◽

Vol 225 ◽

pp. 310-314 ◽

Cited By ~ 1

Author(s):

Mohamad Mahmud Zihad ◽

Kamarul Arifin Ahmad ◽

A. Halim Kadarman

Keyword(s):

Computer Aided Design ◽

Search And Rescue ◽

Aerial Images ◽

Aerial Image ◽

Image System ◽

Prototype Development ◽

Air Vehicle ◽

Military Applications ◽

Image Capturing ◽

Aided Design

This paper presents an ongoing study and research of a 2-Axis stabilized aerial image capturing system to obtain aerial images. Aerial images are commonly used for reconnaissance, area surveying, and also for search and rescue mission. Currently, several methods of remote sensing were developed with multiple objectives either for civil or military applications to obtain high precision images. The study involves the design and fabrication of 2-Axis stabilized image system platform. Rolling and pitching motion of an air vehicle effects while airborne to acquire sharp vertical images are the main consideration in this study.

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A Robust Controller Design for a Tailless Unmanned Air Vehicle with Effective Control Allocation

AIAA Scitech 2021 Forum ◽

10.2514/6.2021-0108 ◽

2021 ◽

Author(s):

Ethem H. Orhan ◽

Kamesh Subbarao

Keyword(s):

Controller Design ◽

Effective Control ◽

Control Allocation ◽

Robust Controller ◽

Unmanned Air Vehicle ◽

Air Vehicle ◽

Robust Controller Design

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Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

Remote Sensing ◽

10.3390/rs11101157 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1157 ◽

Cited By ~ 8

Author(s):

Jorge Fuentes-Pacheco ◽

Juan Torres-Olivares ◽

Edgar Roman-Rangel ◽

Salvador Cervantes ◽

Porfirio Juarez-Lopez ◽

...

Keyword(s):

Precision Agriculture ◽

Image Data ◽

Ground Truth ◽

Aerial Images ◽

Aerial Image ◽

Data Set ◽

Visual Appearance ◽

Aerial Robots ◽

Lighting Conditions ◽

Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

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A Multi-Task Network with Distance–Mask–Boundary Consistency Constraints for Building Extraction from Aerial Images

Remote Sensing ◽

10.3390/rs13142656 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2656

Author(s):

Furong Shi ◽

Tong Zhang

Keyword(s):

Distance Estimation ◽

Image Data ◽

Learning Technologies ◽

Aerial Images ◽

Superior Performance ◽

Aerial Image ◽

Great Success ◽

Building Extraction ◽

Shape Information ◽

Multi Scale

Deep-learning technologies, especially convolutional neural networks (CNNs), have achieved great success in building extraction from areal images. However, shape details are often lost during the down-sampling process, which results in discontinuous segmentation or inaccurate segmentation boundary. In order to compensate for the loss of shape information, two shape-related auxiliary tasks (i.e., boundary prediction and distance estimation) were jointly learned with building segmentation task in our proposed network. Meanwhile, two consistency constraint losses were designed based on the multi-task network to exploit the duality between the mask prediction and two shape-related information predictions. Specifically, an atrous spatial pyramid pooling (ASPP) module was appended to the top of the encoder of a U-shaped network to obtain multi-scale features. Based on the multi-scale features, one regression loss and two classification losses were used for predicting the distance-transform map, segmentation, and boundary. Two inter-task consistency-loss functions were constructed to ensure the consistency between distance maps and masks, and the consistency between masks and boundary maps. Experimental results on three public aerial image data sets showed that our method achieved superior performance over the recent state-of-the-art models.

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