Algorithms of Target Range Track Detection and Tracking

2017 ◽  
pp. 115-148
Author(s):  
Vyacheslav Tuzlukov
Keyword(s):  
Target Range ◽  
Detection And Tracking

scholarly journals Vessel Tracking Using Bistatic Compact HFSWR

2020 ◽  
Vol 12 (8) ◽  
pp. 1266
Author(s):  
Weifeng Sun ◽  
Mengjie Ji ◽  
Weimin Huang ◽  
Yonggang Ji ◽  
Yongshou Dai
Keyword(s):  
Target Tracking ◽  
Development Trend ◽  
Target Range ◽  
Doppler Velocity ◽  
Detection Accuracy ◽  
Kinematic Parameters ◽  
Coverage Area ◽  
Detection And Tracking ◽  
Wave Radar ◽  
Target Track

Bistatic and multi-static high-frequency surface wave radar (HFSWR) is becoming a prospective development trend for sea surface surveillance due to its potential in extending the coverage area, improving the detection accuracy, etc. In this paper, the vessel detection and tracking performance of a newly developed bistatic compact HFSWR system whose transmitting and receiving antennas are not co-located was investigated. Firstly, the representation of the target range and Doppler velocity concerning a bistatic HFSWR was derived and compared with that of a monostatic system. Next, taking the characteristics of target kinematic parameters into account, a target tracking method applicable to a bistatic HFSWR is proposed. The simultaneous target tracking results from both monostatic and bistatic HFSWR field data are presented and compared. The experimental results demonstrate the good performance in target tracking of the bistatic HFSWR and also show that an HFSWR system combining monostatic and bistatic modes has the potential to enhance the target track continuity and improve the detection accuracy.

scholarly journals Airborne Visual Detection and Tracking of Cooperative UAVs Exploiting Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4332 ◽  
Author(s):  
Roberto Opromolla ◽  
Giuseppe Inchingolo ◽  
Giancarmine Fasano
Keyword(s):  
Deep Learning ◽  
Visual Detection ◽  
Detection System ◽  
Flight Test ◽  
Target Range ◽  
Detection And Tracking ◽  
Military Applications ◽  
Technological Challenge ◽  
High Level ◽  
Processing Architecture

The performance achievable by using Unmanned Aerial Vehicles (UAVs) for a large variety of civil and military applications, as well as the extent of applicable mission scenarios, can significantly benefit from the exploitation of formations of vehicles able to fly in a coordinated manner (swarms). In this respect, visual cameras represent a key instrument to enable coordination by giving each UAV the capability to visually monitor the other members of the formation. Hence, a related technological challenge is the development of robust solutions to detect and track cooperative targets through a sequence of frames. In this framework, this paper proposes an innovative approach to carry out this task based on deep learning. Specifically, the You Only Look Once (YOLO) object detection system is integrated within an original processing architecture in which the machine-vision algorithms are aided by navigation hints available thanks to the cooperative nature of the formation. An experimental flight test campaign, involving formations of two multirotor UAVs, is conducted to collect a database of images suitable to assess the performance of the proposed approach. Results demonstrate high-level accuracy, and robustness against challenging conditions in terms of illumination, background and target-range variability.

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
Sign in / Sign up

Export Citation Format

Share Document