Real-time moving objects tracking for mobile-robots using motion information

2014 ◽  
Author(s):  
Mahmoud A. Mohamed ◽  
Christoph Boddeker ◽  
Barbel Mertsching
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Moving Objects ◽  
Motion Information ◽  
Objects Tracking

Real-Time Moving Objects Tracking for Distributed Smart Video Surveillances

2016 ◽  
Vol 11 (4) ◽  
pp. 324
Author(s):  
Nor Nadirah Abdul Aziz ◽  
Yasir Mohd Mustafah ◽  
Amelia Wong Azman ◽  
Amir Akramin Shafie ◽  
Muhammad Izad Yusoff ◽  
...  
Keyword(s):  
Real Time ◽  
Moving Objects ◽  
Objects Tracking

MULTIPLE-TARGET CLASSIFICATION AND TRACKING FOR MOBILE ROBOTS USING A 2D LASER RANGE SCANNER

2012 ◽  
Vol 09 (04) ◽  
pp. 1250025 ◽  
Author(s):  
POLYCHRONIS KONDAXAKIS ◽  
HARIS BALTZAKIS
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Moving Objects ◽  
Data Association ◽  
Human Robot Interaction ◽  
Range Data ◽  
Multiple Model ◽  
Target Classification ◽  
Laser Range ◽  
Fuzzy Art

In human–robot interaction developments, detection, tracking and identification of moving objects (DATMO) constitute an important problem. More specifically, in mobile robots this problem becomes harder and more computationally expensive as the environments become dynamic and more densely populated. The problem can be divided into a number of sub-problems, which include the compensation of the robot's motion, measurement clustering, feature extraction, data association, targets' trajectory estimation and finally, target classification. Here, a mobile robot uses 2D laser range data to identify and track moving targets. A Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) tracking algorithm associates the available laser data to track and provide an estimated state vector of targets' position and velocity. Potential moving objects are initially learned in a supervised manner and later on are autonomously classified in real-time using a trained Fuzzy ART neural network classifier. The recognized targets are fed back to the tracker to further improve the track initiation process. The resulting technique introduces a computationally efficient approach to already existing target-tracking and identification research, which is especially suited for real time application scenarios.

scholarly journals Intelligent Real-Time Deep System for Robust Objects Tracking in Low-Light Driving Scenario

Computation ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 117
Author(s):  
Francesco Rundo
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Moving Objects ◽  
Hybrid Architecture ◽  
Collision Risk ◽  
Low Light ◽  
Road Signs ◽  
Road Tunnels ◽  
Objects Tracking ◽  
Driving Scenario

The detection of moving objects, animals, or pedestrians, as well as static objects such as road signs, is one of the fundamental tasks for assisted or self-driving vehicles. This accomplishment becomes even more difficult in low light conditions such as driving at night or inside road tunnels. Since the objects found in the driving scene represent a significant collision risk, the aim of this scientific contribution is to propose an innovative pipeline that allows real time low-light driving salient objects tracking. Using a combination of the time-transient non-linear cellular networks and deep architectures with self-attention, the proposed solution will be able to perform a real-time enhancement of the low-light driving scenario frames. The downstream deep network will learn from the frames thus improved in terms of brightness in order to identify and segment salient objects by bounding-box based approach. The proposed algorithm is ongoing to be ported over a hybrid architecture consisting of a an embedded system with SPC5x Chorus MCU integrated with an automotive-grade system based on STA1295 MCU core. The performances (accuracy of about 90% and correlation coefficient of about 0.49) obtained in the experimental validation phase confirmed the effectiveness of the proposed method.

A novel real-time analyzer for moving objects on freeway

2002 ◽  
Author(s):  
Jyh Chen ◽  
Jin-Tu Huang ◽  
Hsing-Chin Yeh ◽  
Chean-Mean Chen ◽  
Yen-Tseng Hsu
Keyword(s):  
Real Time ◽  
Moving Objects ◽  
Time Analyzer

The Study and Application of the Nitrile Gloves’ Production Line’s Online Measurement

2015 ◽  
Vol 727-728 ◽  
pp. 804-808
Author(s):  
Jian Jun Cai ◽  
Jian Bing Han ◽  
Su Qin Qu
Keyword(s):  
Temperature Measurement ◽  
Real Time ◽  
Temperature Sensor ◽  
Production Line ◽  
Moving Objects ◽  
Mold Temperature ◽  
Configuration Software ◽  
Online Measurement ◽  
Line Process ◽  
Tracking Technology

In order to solve the problem of the hand module’s online temperature measurement in Nitrile glove production line process, we have developed a set of moving objects or device used for online temperature measurement. The device is mounted infrared temperature sensor, by using PLC-controlled synchronous tracking technology it can track the hand module in the production line, so it can ensure the infrared temperature sensor and hand module synchronously measure its temperature correctly. By using configuration software kingview, we have developed mold temperature monitoring online measurement interface, real-time monitoring of the mold temperature opponents. After analysis of experimental results we concluded that the device work stable, acquisition of temperature reliably, so it has a promotional value.

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