scholarly journals Rigid Object Tracking Algorithms for Low-Cost AR Devices

2014 ◽  
Author(s):  
Timothy Garrett ◽  
Saverio Debernardis ◽  
Rafael Radkowski ◽  
Carl K. Chang ◽  
Michele Fiorentino ◽  
...  
Keyword(s):  
Object Tracking ◽  
Low Cost ◽  
Internal Representation ◽  
Object Identification ◽  
Sparse Representations ◽  
Microsoft Kinect ◽  
Future Research ◽  
Rigid Object ◽  
Tracking Method ◽  
Mesh Reconstruction

Augmented reality (AR) applications rely on robust and efficient methods for tracking. Tracking methods use a computer-internal representation of the object to track, which can be either sparse or dense representations. Sparse representations use only a limited set of feature points to represent an object to track, whereas dense representations almost mimic the shape of an object. While algorithms performed on sparse representations are faster, dense representations can distinguish multiple objects. The research presented in this paper investigates the feasibility of a dense tracking method for rigid object tracking, which incorporates the both object identification and object tracking steps. We adopted a tracking method that has been developed for the Microsoft Kinect to support single object tracking. The paper describes this method and presents the results. We also compared two different methods for mesh reconstruction in this algorithm. Since meshes are more informative when identifying a rigid object, this comparison indicates which algorithm shows the best performance for this task and guides our future research efforts.

Research of ASM Object Tracking Method Combining Kalman Estimation

2014 ◽  
Vol 1049-1050 ◽  
pp. 1595-1598
Author(s):  
Li Guo Zhang ◽  
Mei Jin ◽  
Ju Jin ◽  
Guo Hui Yu
Keyword(s):  
Kalman Filter ◽  
Statistical Model ◽  
Object Tracking ◽  
Experimental Results ◽  
Initial Contour ◽  
Initial State ◽  
Current Frame ◽  
Rigid Object ◽  
Tracking Method ◽  
Actual Contour

ASM is a statistical model applied to match contours of non-rigid object. The actual contour may much different from the initial contour and the result is likely to converge to an error contour. Kalman filter is adopted to track the current frame for the prediction and acts as the initial state of the ASM, and then applies the ASM to correct the contour of the object. Experimental results show that the method proposed in this paper allows the model to converge to the target contour quickly and accurately. It has good stability and robustness.

Combining Ultrasonic Signals And Multi-Colored Images To Perform Object Tracking And Recognition In Low-Cost Robotic Platforms

2016 ◽  
Author(s):  
Danilo H. F. Menezes ◽  
Thiago D. Mendonca ◽  
Wolney M. Neto ◽  
Hendrik T. Macedo ◽  
Leonardo N. Matos
Keyword(s):  
Object Tracking ◽  
Low Cost ◽  
Ultrasonic Signals ◽  
Robotic Platforms ◽  
Tracking And Recognition

Motivating Compliance: Firm Response to Mandatory Disclosure Policies

2018 ◽  
Vol 32 (2) ◽  
pp. 103-119
Author(s):  
Colleen M. Boland ◽  
Chris E. Hogan ◽  
Marilyn F. Johnson
Keyword(s):  
Conflict Of Interest ◽  
Low Cost ◽  
Cost Effective ◽  
Data Availability ◽  
Policy Adoption ◽  
Future Research ◽  
Regulated Firms ◽  
Firm Response ◽  
Disclosure Rules ◽  
The Impact

SYNOPSIS Mandatory existence disclosure rules require an organization to disclose a policy's existence, but not its content. We examine policy adoption frequencies in the year immediately after the IRS required mandatory existence disclosure by nonprofits of various governance policies. We also examine adoption frequencies in the year of the subsequent change from mandatory existence disclosure to a disclose-and-explain regime that required supplemental disclosures about the content and implementation of conflict of interest policies. Our results suggest that in areas where there is unclear regulatory authority, mandatory existence disclosure is an effective and low cost regulatory device for encouraging the adoption of policies desired by regulators, provided those policies are cost-effective for regulated firms to implement. In addition, we find that disclose-and-explain regulatory regimes provide stronger incentives for policy adoption than do mandatory existence disclosure regimes and also discourage “check the box” behavior. Future research should examine the impact of mandatory existence disclosure rules in the year that the regulation is implemented. Data Availability: Data are available from sources cited in the text.

Multi-object Tracking Method Based on Efficient Channel Attention and Switchable Atrous Convolution

2021 ◽  
Author(s):  
Xuezhi Xiang ◽  
Wenkai Ren ◽  
Yujian Qiu ◽  
Kaixu Zhang ◽  
Ning Lv
Keyword(s):  
Object Tracking ◽  
Tracking Method

scholarly journals A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 517
Author(s):  
Seong-heum Kim ◽  
Youngbae Hwang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Low Cost ◽  
Detection Methods ◽  
Future Research ◽  
3D Object ◽  
Practical Applications ◽  
Depth Sensors ◽  
Significant Research ◽  
3D Object Detection

Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection. For relatively low-cost data acquisition systems without depth sensors or cameras at multiple viewpoints, we first consider existing databases with 2D RGB photos and their relevant attributes. Based on this simple sensor modality for practical applications, deep learning-based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized. We present the key concepts and detailed descriptions of representative single-stage and multiple-stage detection solutions. In addition, we discuss the effectiveness of the detection models on their baseline benchmarks. Finally, we explore several directions for future research on monocular 3D object detection.

scholarly journals Low-temperature-processed metal oxide electron transport layers for efficient planar perovskite solar cells

Rare Metals ◽  
2021 ◽  
Author(s):  
Jia-Xing Song ◽  
Xin-Xing Yin ◽  
Zai-Fang Li ◽  
Yao-Wen Li
Keyword(s):  
Solar Cells ◽  
Electron Transport ◽  
Low Temperature ◽  
Metal Oxide ◽  
Low Cost ◽  
Perovskite Solar Cells ◽  
Transport Layer ◽  
Electron Transport Layer ◽  
Future Research ◽  
Low Temperature Preparation

Abstract As a promising photovoltaic technology, perovskite solar cells (pero-SCs) have developed rapidly over the past few years and the highest power conversion efficiency is beyond 25%. Nowadays, the planar structure is universally popular in pero-SCs due to the simple processing technology and low-temperature preparation. Electron transport layer (ETL) is verified to play a vital role in the device performance of planar pero-SCs. Particularly, the metal oxide (MO) ETL with low-cost, superb versatility, and excellent optoelectronic properties has been widely studied. This review mainly focuses on recent developments in the use of low-temperature-processed MO ETLs for planar pero-SCs. The optical and electronic properties of widely used MO materials of TiO2, ZnO, and SnO2, as well as the optimizations of these MO ETLs are briefly introduced. The commonly used methods for depositing MO ETLs are also discussed. Then, the applications of different MO ETLs on pero-SCs are reviewed. Finally, the challenge and future research of MO-based ETLs toward practical application of efficient planar pero-SCs are proposed. Graphical abstract

scholarly journals Multiple Pedestrians and Vehicles Tracking in Aerial Imagery Using a Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1953
Author(s):  
Seyed Majid Azimi ◽  
Maximilian Kraus ◽  
Reza Bahmanyar ◽  
Peter Reinartz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Object Tracking ◽  
Short Term Memory ◽  
Aerial Imagery ◽  
Future Research ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-resolution aerial imagery by intensive evaluation of a number of traditional and Deep Learning based Single- and Multi-Object Tracking methods. We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for more accurate and stable tracking. Moreover, we investigate the influence of the Squeeze-and-Excitation layers and Online Hard Example Mining on the performance of AerialMPTNet. To the best of our knowledge, we are the first to use these two for regression-based Multi-Object Tracking. Additionally, we studied and compared the L1 and Huber loss functions. In our experiments, we extensively evaluate AerialMPTNet on three aerial Multi-Object Tracking datasets, namely AerialMPT and KIT AIS pedestrian and vehicle datasets. Qualitative and quantitative results show that AerialMPTNet outperforms all previous methods for the pedestrian datasets and achieves competitive results for the vehicle dataset. In addition, Long Short-Term Memory and Graph Convolutional Neural Network modules enhance the tracking performance. Moreover, using Squeeze-and-Excitation and Online Hard Example Mining significantly helps for some cases while degrades the results for other cases. In addition, according to the results, L1 yields better results with respect to Huber loss for most of the scenarios. The presented results provide a deep insight into challenges and opportunities of the aerial Multi-Object Tracking domain, paving the way for future research.

Multi-Scale Anti-Occlusion Correlation Filters Object Tracking Method Based on Complementary Features

2020 ◽  
pp. 2155002
Author(s):  
Xiuhua Hu ◽  
Yuan Chen ◽  
Yan Hui ◽  
Yingyu Liang ◽  
Guiping Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Scale Model ◽  
Correlation Filters ◽  
Motion Feature ◽  
Tracking Method ◽  
Object State ◽  
Multi Scale ◽  
Tracking Process ◽  
Object Appearance ◽  
Response Peak

Aiming to tackle the problem of tracking drift easily caused by complex factors during the tracking process, this paper proposes an improved object tracking method under the framework of kernel correlation filter. To achieve discriminative information that is not sensitive to object appearance change, it combines dimensionality-reduced Histogram of Oriented Gradients features and Lab color features, which can be used to exploit the complementary characteristics robustly. Based on the idea of multi-resolution pyramid theory, a multi-scale model of the object is constructed, and the optimal scale for tracking the object is found according to the confidence maps’ response peaks of different sizes. For the case that tracking failure can easily occur when there exists inappropriate updating in the model, it detects occlusion based on whether the occlusion rate of the response peak corresponding to the best object state is less than a set threshold. At the same time, Kalman filter is used to record the motion feature information of the object before occlusion, and predict the state of the object disturbed by occlusion, which can achieve robust tracking of the object affected by occlusion influence. Experimental results show the effectiveness of the proposed method in handling various internal and external interferences under challenging environments.

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