scholarly journals Language Detection and Tracking in Multilingual Documents Using Weak Estimators

2010 ◽  
pp. 600-609 ◽  
Author(s):  
Aleksander Stensby ◽  
B. John Oommen ◽  
Ole-Christoffer Granmo
Keyword(s):  
Detection And Tracking ◽  
Language Detection

THE USE OF WEAK ESTIMATORS TO ACHIEVE LANGUAGE DETECTION AND TRACKING IN MULTILINGUAL DOCUMENTS

2013 ◽  
Vol 27 (04) ◽  
pp. 1350011 ◽  
Author(s):  
ALEKSANDER STENSBY ◽  
B. JOHN OOMMEN ◽  
OLE-CHRISTOFFER GRANMO
Keyword(s):  
Word Of Mouth ◽  
Current Problem ◽  
Real Life ◽  
Estimation Methods ◽  
Data Sets ◽  
Short Word ◽  
Conditional Distributions ◽  
Detection And Tracking ◽  
The World ◽  
Language Detection

This paper deals with the problems of language detection and tracking in multilingual online short word-of-mouth (WoM) discussions. This problem is particularly unusual and difficult from a pattern recognition perspective because, in these discussions, the participants and content involve the opinions of users from all over the world. The nature of these discussions, consisting of multiple topics in different languages, presents us with a problem of finding training and classification strategies when the class-conditional distributions are nonstationary. The difficulties in solving the problem are many-fold. First of all, the analyst has no knowledge of when one language stops and when the next starts. Further, the features which one uses for any one language (for example, the n-grams) will not be valid to recognize another. Finally, and most importantly, in most real-life applications, such as in WoM, the fragments of text available before the switching, are so small that it renders any meaningful classification using traditional estimation methods almost futile. Earlier, the authors [B. J. Oommen and L. Rueda, Patt. Recogn.39(1) (2006) 328–341.] had recommended that for a variety of problems, the use of strong estimators (i.e. estimators that converge with probability 1) is sub-optimal. In this vein, we propose to solve the current problem using novel estimators that are pertinent for nonstationary environments. The classification results obtained for various data sets which involve as many as eight languages demonstrates that our proposed methodology is both powerful and efficient.

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

HUMAN FALL DETECTION AND TRACKING SYSTEM USING MEMS SENSOR

2017 ◽  
Vol 6 (3) ◽  
pp. 20
Author(s):  
A. SAIPRIYA ◽  
V. MEENA ◽  
MAALIK M.ABDUL ◽  
D. PRAVINRAJ ◽  
P. JEGADEESHWARI ◽  
...  
Keyword(s):  
Tracking System ◽  
Fall Detection ◽  
Mems Sensor ◽  
Detection And Tracking

A Language Detection System for Short Chats in Mobile Games

2015 ◽  
Author(s):  
Pidong Wang ◽  
Nikhil Bojja ◽  
Shivasankari Kannan
Keyword(s):  
Detection System ◽  
Mobile Games ◽  
Language Detection

A Robust and Efficient Video Moving Object Detection and Tracking Algorithm

2009 ◽  
Vol 35 (8) ◽  
pp. 1055-1062
Author(s):  
Shao-Hua LIU ◽  
Mao-Jun ZHANG ◽  
Zhi-Hui XIONG ◽  
Wang CHEN
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Tracking Algorithm ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Efficient Video
Sign in / Sign up

Export Citation Format

Share Document