Paper Info
Title
Inexpensive ground truth and performance evaluation for human tracking using multiple laser measurement sensors
Abstract
This paper will describe a flexible and inexpensive method of obtaining ground truth for the evaluation of Human Tracking systems. It is expected to be appropriate for evaluating systems used to allow robots and/or autonomous vehicles to operate safely around humans. It is currently focused on tracking people as they stand still or walk. It relies on multiple Laser Measurement Sensors(LMS) also called laser line scanners. The LMS's are mounted to scan in a horizontal plane. A method for quickly calibrating the relative position and orientation of each of the sensors to each other is described. A basic human tracking algorithm using the LMS's is described along with how the algorithm can be combined with a priori knowledge of the walkers intended path during the test. A graphical user interface(GUI) displays both the data obtained directly from the LMS and the output of the tracking algorithm. The GUI allows the user to verify and adjust the tracking algorithm without needing to annotate every frame, and therefore at a lower cost than systems that require extensive annotation. Tests were performed with people walking or running though several patterns, while data was simultaniously recorded by a more expensive system require individual receivers on each participant for comparison.
Year
DOI
Venue
2010
10.1145/2377576.2377613
PerMIS
Keywords
Field
DocType
multiple laser measurement sensor,autonomous vehicle,extensive annotation,expensive system,ground truth,inexpensive ground truth,basic human tracking algorithm,horizontal plane,human tracking system,inexpensive method,performance evaluation,graphical user interface,tracking algorithm
Computer vision,Computer science,Simulation,A priori and a posteriori,Tracking system,Laser,Graphical user interface,Ground truth,Artificial intelligence,Robot,Horizontal plane,Calibration
Conference
Citations 
PageRank 
References 
1
0.63
3
Authors
3
Name
Order
Citations
PageRank
William Shackleford110.96
Tsai Hong213714.46
Tommy Chang3588.34