Evaluation of a Laser-based Multi-people Detection and Tracking System

2006 ◽  
Author(s):  
Jinshi Cui ◽  
Hongbin Zha ◽  
Huijing Zhao ◽  
Ryosuke Shibasaki
Keyword(s):  
Tracking System ◽  
People Detection ◽  
Detection And Tracking

A Novel Method of Motion People Detection and Tracking in Moving Background

2013 ◽  
Vol 373-375 ◽  
pp. 547-551 ◽  
Author(s):  
Lve Huang ◽  
Hua Biao Yan ◽  
Lu Min Tan
Keyword(s):  
Fuzzy Control ◽  
Real Time ◽  
Tracking System ◽  
Polar Coordinates ◽  
Model Matching ◽  
People Tracking ◽  
People Detection ◽  
Detection And Tracking ◽  
Novel Method ◽  
Background Update

The Surendra background update and novel fast model matching were mixed which can reduce the matching region. A Yuntai tracking system was present for people tracking, the fuzzy control tracking based on polar coordinates was also present, which makes the tracking of people object always in the video range and the Yuntai neednt move frequency. Results indicate that the algorithm is superior to the previously published variants of the model matching and the Yuntai system track people in real time.

scholarly journals Indoor Detection and Tracking of People Using mmWave Sensor

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xu Huang ◽  
Hasnain Cheena ◽  
Abin Thomas ◽  
Joseph K. P. Tsoi
Keyword(s):  
Tracking System ◽  
Clustering Algorithms ◽  
Computation Time ◽  
Raspberry Pi ◽  
Tracking Accuracy ◽  
Experimental Accuracy ◽  
People Detection ◽  
Position Errors ◽  
Detection And Tracking ◽  
Average Position

This paper proposes a new indoor people detection and tracking system using a millimeter-wave (mmWave) radar sensor. Firstly, a systematic approach for people detection and tracking is presented—a static clutter removal algorithm used for removing mmWave radar data’s static points. Two efficient clustering algorithms are used to cluster and identify people in a scene. The recursive Kalman filter tracking algorithm with data association is used to track multiple people simultaneously. Secondly, a fast indoor people detection and tracking system is designed based on our proposed algorithms. The method is lightweight enough for scalability and portability, and we can execute it in real time on a Raspberry Pi 4. Finally, the proposed method is validated by comparing it with the Texas Instruments (TI) system. The proposed system’s experimental accuracy ranged from 98% (calculated by misclassification errors) for one person to 65% for five people. The average position errors at positions 1, 2, and 3 are 0.2992 meters, 0.3271 meters, and 0.3171 meters, respectively. In comparison, the Texas Instruments system had an experimental accuracy ranging from 96% for one person to 45% for five people. The average position errors at positions 1, 2, and 3 are 0.3283 meters, 0.3116 meters, and 0.3343 meters, respectively. The proposed method’s advantage is demonstrated in terms of tracking accuracy, computation time, and scalability.

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