A novel method for segmenting moving objects in aerial imagery using matrix recovery and physical spring model

2016 ◽  
Author(s):  
Agwad ElTantawy ◽  
Mohamed S. Shehata
Keyword(s):  
Moving Objects ◽  
Aerial Imagery ◽  
Spring Model ◽  
Matrix Recovery ◽  
Novel Method

scholarly journals A novel method of 3D motion object’s position perception

2018 ◽  
Vol 246 ◽  
pp. 03020
Author(s):  
Tan Wei ◽  
Xuan Liu ◽  
Chen Yi ◽  
Erfu Yang
Keyword(s):  
Measurement Method ◽  
Moving Objects ◽  
Target Object ◽  
Monocular Vision ◽  
Position Information ◽  
Sorting Method ◽  
3D Objects ◽  
The Disabled ◽  
Novel Method ◽  
Location Measurement

With the development of industrial automation, location measurement of 3D objects is becoming more and more important, especially as it can provide necessary positional parameters for the manipulator to grasp the object accurately. In view of the disabled object which is in widespread use currently, its image is captured to obtain positional parameters and transmitted to manipulators in industry. The above process is delayed, affecting the work efficiency of the manipulator. A method for calculating the position information of target object in motion is proposed. This method uses monocular vision technology to track 3D moving objects，then uses contour sorting method to extract the minimum constrained contour rectangle, and combines the video alignment technology to realize the tracking. Thus, the measurement error is reduced. The experimental results and analysis show that the adopted measurement method is effective.

A novel method for full position and angular orientation measurement of moving objects

2015 ◽  
Author(s):  
Harbans S. Dhadwal ◽  
Jahangir Rastegar ◽  
Dake Feng ◽  
Philip Kwok ◽  
Carlos M. Pereira
Keyword(s):  
Moving Objects ◽  
Angular Orientation ◽  
Orientation Measurement ◽  
Novel Method

scholarly journals Humanrecognition Using ‘Pso-Ofa’ In Low Resolution Videos

2021 ◽  
Vol 12 (11) ◽  
pp. 697-703
Author(s):  
K.Ranga Narayana, Et. al.
Keyword(s):  
Optical Flow ◽  
Moving Objects ◽  
Previous Method ◽  
Video Data ◽  
Detection Methods ◽  
Support Vector ◽  
Low Resolution ◽  
Swarm Optimization ◽  
Detection Approach ◽  
Novel Method

In present scenario, tracking of target in videos with low resolution is most important task.  The problem aroused due to lack of discriminatory data that have low visual visibility of the moving objects. However, earlier detection methods often extract explanations around fascinating points of space or exclude mathematical features in moving regions, resulting in limited capabilities to detect better video functions. To overcome the above problem, in this paper a novel method which recognizes a person from low resolution videos is proposed. A Three step process is implemented in which during the first step, the video data acquired from a low-resolution video i.e. from three different datasets. The acquired video is divided into frames and converted into gray scale from RGB. Secondly, background subtraction is performed using LBP and thereafter Histogram of Optical Flow (HOF) descriptors is extracted from optical flow images for motion estimation. In the third step, the eigen features are extracted and optimized using particle swarm optimization (PSO) model to eliminate redundant information and obtain optimized features from the video which is being processed. Finally to find a person from low resolution videos, the features are classified by Support Vector Machine (SVM) and parameters are evaluated. Experimental results are performed on VIRAT, Soccer and KTH datasets and demonstrated that the proposed detection approach is superior to the previous method

scholarly journals A Novel Method of Localization for Moving Objects with an Alternating Magnetic Field

Sensors ◽  
2017 ◽  
Vol 17 (4) ◽  
pp. 923 ◽  
Author(s):  
Xiang Gao ◽  
Shenggang Yan ◽  
Bin Li
Keyword(s):  
Magnetic Field ◽  
Moving Objects ◽  
Alternating Magnetic Field ◽  
Novel Method

scholarly journals Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3371 ◽  
Author(s):  
Hossain ◽  
Lee
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Moving Objects ◽  
State Of The Art ◽  
Target Position ◽  
Guidance System ◽  
Aerial Imagery ◽  
Detection And Tracking ◽  
Real Time Tracking

In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-the-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one was designed using a Jetson TX or AGX Xavier, and the other was based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-the-art deep learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates, as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep learning-based association metric approach with simple online and real-time tracking (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.

A Moving Object Detection Process for Computer Vision Application

1992 ◽  
Vol 206 (1) ◽  
pp. 33-39
Author(s):  
I Shieh ◽  
K F Gill
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Testing Procedure ◽  
Moving Object ◽  
Current Frame ◽  
Object Movement ◽  
Hypothesis Testing Procedure ◽  
Pertinent Information ◽  
Novel Method ◽  
Vision Application

The aim of this paper is to present a novel method for processing a digitized image that will allow pertinent information to be extracted on object movement in a scene. A frame difference method locates the moving candidates in a region which is evaluated by a hypothesis testing procedure to identify accretion and deletion regions. Accretion regions are selected and used as seeds to search for moving objects in the current frame. Contour tracing is applied to establish the boundary of an accretion region which is then used to help recognize the moving object. The results of this work reveal that motion can be used as an effective cue for object detection from an image sequence.

scholarly journals Usability of aerial video footage for 3D-scene reconstruction and structural damage assessment

2018 ◽  
Author(s):  
Johnny Cusicanqui ◽  
Norman Kerle ◽  
Francesco Nex
Keyword(s):  
Damage Assessment ◽  
Structural Damage ◽  
Moving Objects ◽  
Point Clouds ◽  
Video Data ◽  
Aerial Imagery ◽  
Video Footage ◽  
3D Point Clouds ◽  
Post Disaster ◽  
Aerial Video

Abstract. Remote sensing has evolved into the most efficient approach to assess post-disaster structural damage, in extensively affected areas through the use of space-borne data. For smaller, and in particular, complex urban disaster scenes, multi-perspective aerial imagery obtained with Unmanned Aerial Vehicles and derived dense colour 3D-models are increasingly being used. These type of data allow the direct and automated recognition of damage-related features, supporting an effective post-disaster structural damage assessment. However, the rapid collection and sharing of multi-perspective aerial imagery is still limited due to tight or lacking regulations and legal frameworks. A potential alternative is aerial video footage, typically acquired and shared by civil protection institutions or news media, and which tend to be the first type of airborne data available. Nevertheless, inherent artifacts and the lack of suitable processing means, have long limited its potential use in structural damage assessment and other post-disaster activities. In this research the usability of modern aerial video data was evaluated based on a comparative quality and application analysis of video data and multi-perspective imagery (photos), and their derivative 3D point clouds created using current photogrammetric techniques. Additionally, the effects of external factors, such as topography and the presence of smoke and moving objects were determined by analyzing two different earthquake-affected sites: Tainan (Taiwan) and Pescara del Tronto (Italy). Results demonstrated similar usabilities for video and photos. This is shown by the short 2 cm of difference between the accuracies of video and photo-based 3D Point clouds. Despite the low video resolution, the usability of this data was compensated by a small ground sampling distance. Instead of video characteristics, low quality and application resulted from non-data related factors, such as changes in the scene, lack of texture or moving objects. We conclude that current video data are not only more rapidly available than photos, but they also have a comparable ability to assist in image-based structural damage assessment and other post-disaster activities.

scholarly journals Reconstruction of a fluttering flag from a single image

2021 ◽  
Vol 15 ◽  
pp. 174830262098365
Author(s):  
Tao Hu ◽  
Jun Li ◽  
Guihuan Guo
Keyword(s):  
Geometric Structure ◽  
Depth Map ◽  
Spring Model ◽  
Neural Fields ◽  
Single Image ◽  
Structure Information ◽  
Alpha Shape ◽  
Novel Method ◽  
Delaunay Mesh

Reconstructing a 3 D object from a single image is a challenging task because determining useful geometric structure information from a single image is difficult. In this paper, we propose a novel method to extract the 3 D mesh of a flag from a single image and drive the flag model to flutter with virtual wind. A deep convolutional neural fields model is first used to generate a depth map of a single image. Based on the Alpha Shape, a coarse 2 D mesh of flag is reconstructed by sampling at different depth regions. Then, we optimize the mesh to generate a mesh with depth based on Restricted Frontal-Delaunay. We transform the Delaunay mesh with depth into a simple spring model and use a velocity-based solver to calculate the moving position of the virtual flag model. The experiments demonstrate that the proposed method can construct a realistic fluttering flag video from a single image.

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