pyTAG: python-based interactive training data generation for visual tracking algorithms (Conference Presentation)

2020 ◽  
Author(s):  
Ekincan Ufuktepe ◽  
Vipul Ramtekkar ◽  
Ke Gao ◽  
Noor Al-Shakarji ◽  
Joshua Fraser ◽  
...  
Keyword(s):  
Visual Tracking ◽  
Training Data ◽  
Data Generation ◽  
Tracking Algorithms ◽  
Interactive Training

scholarly journals BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2144
Author(s):  
Stefan Reitmann ◽  
Lorenzo Neumann ◽  
Bernhard Jung
Keyword(s):  
Point Clouds ◽  
Training Data ◽  
Sensor Data ◽  
Generation Process ◽  
Data Generation ◽  
Depth Sensor ◽  
Depth Sensing ◽  
Depth Sensors ◽  
3D Point Clouds ◽  
Wide Range

Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

scholarly journals Creating Hardware Component Knowledge Bases with Training Data Generation and Multi-task Learning

2020 ◽  
Vol 19 (6) ◽  
pp. 1-26
Author(s):  
Luke Hsiao ◽  
Sen Wu ◽  
Nicholas Chiang ◽  
Christopher Ré ◽  
Philip Levis
Keyword(s):  
Knowledge Bases ◽  
Training Data ◽  
Data Generation ◽  
Task Learning ◽  
Hardware Component

Synthetic AI training data generation enabling airfield damage assessment

2021 ◽  
Author(s):  
Logan Cannan ◽  
Brian M. Robinson ◽  
Kathryn Patterson ◽  
Darrell Langford ◽  
Robert Diltz ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Training Data ◽  
Data Generation

scholarly journals VEHICLE LOCALIZATION BY LIDAR POINT CORRELATION IMPROVED BY CHANGE DETECTION

2016 ◽  
Vol XLI-B1 ◽  
pp. 703-710 ◽  
Author(s):  
A. Schlichting ◽  
C. Brenner
Keyword(s):  
Change Detection ◽  
Reference Data ◽  
Correlation Method ◽  
Simulated Data ◽  
Detection Algorithm ◽  
Training Data ◽  
Image Correlation ◽  
Data Generation ◽  
Vehicle Localization ◽  
Dynamic Objects

LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. <br><br> For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.

A Dynamic Subspace Anomaly Detection Method Using Generic Algorithm for Streaming Network Data

2015 ◽  
pp. 403-425
Author(s):  
Ji Zhang
Keyword(s):  
Anomaly Detection ◽  
Data Streams ◽  
Training Data ◽  
Detection Methods ◽  
Network Data ◽  
Data Generation ◽  
Research Attention ◽  
Network Connection ◽  
Dimensional Network ◽  
Anomaly Classification

A great deal of research attention has been paid to data mining on data streams in recent years. In this chapter, the authors carry out a case study of anomaly detection in large and high-dimensional network connection data streams using Stream Projected Outlier deTector (SPOT) that is proposed in Zhang et al. (2009) to detect anomalies from data streams using subspace analysis. SPOT is deployed on 1999 KDD CUP anomaly detection application. Innovative approaches for training data generation, anomaly classification, false positive reduction, and adoptive detection subspace generation are proposed in this chapter as well. Experimental results demonstrate that SPOT is effective and efficient in detecting anomalies from network data streams and outperforms existing anomaly detection methods.

scholarly journals A Novel Approach for Handling Outliers in Imbalanced Data

2018 ◽  
Vol 7 (3.1) ◽  
pp. 1 ◽  
Author(s):  
Gillala Rekha ◽  
V Krishna Reddy
Keyword(s):  
Class Imbalance ◽  
Training Data ◽  
Classification Algorithms ◽  
Main Concern ◽  
Data Generation ◽  
Class Imbalance Problem ◽  
Class Distribution ◽  
Novel Approach ◽  
Real World Datasets ◽  
Data Level

Most of the traditional classification algorithms assume their training data to be well-balanced in terms of class distribution. Real-world datasets, however, are imbalanced in nature thus degrade the performance of the traditional classifiers. To solve this problem, many strategies are adopted to balance the class distribution at the data level. The data level methods balance the imbalance distribution between majority and minority classes using either oversampling or under sampling techniques. The main concern of this paper is to remove the outliers that may generate while using oversampling techniques. In this study, we proposed a novel approach for solving the class imbalance problem at data level by using modified SMOTE to remove the outliers that may exist after synthetic data generation using SMOTE oversampling technique. We extensively compare our approach with SMOTE, SMOTE+ENN, SMOTE+Tomek-Link using 9 datasets from keel repository using classification algorithms. The result reveals that our approach improves the prediction performance for most of the classification algorithms and achieves better performance compared to the existing approaches.   

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