Name
Abstract | ||
|---|---|---|
We investigate human behavior-based target tracking from omni-directional (O-D) thermal images for intelligent perception in unmanned systems. Current target tracking approaches are primarily focused on perspective visual and infrared (IR) band, as well as O-D visual band tracking. The target tracking from O-D images and the use of O-D thermal vision have not been adequately addressed. Thermal O-D images provide a number of advantages over other passive sensor modalities such as illumination invariance, wide field-of-view, ease of identifying heat-emitting objects, and long term tracking without interruption. Unfortunately, thermal O-D sensors have not yet been widely used due to the following disadvantages: low resolution, low frame rates, high cost, sensor noise, and an increase in tracking time. This paper outlines a spectrum of approaches which mitigate these disadvantages to enable an O-D thermal IR camera equipped with a mobile robot to track a human in a variety of environments and conditions. The curve matched Kalman filter is used for tracking a human target based on the behavioral movement of the human and
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maximum a posteriori</italic>
(MAP)-based estimation is extended for the human tracking as long term which provides a faster prediction. The benefits to using our MAP-based method are decreasing the prediction time of a target’s position and increasing the accuracy of prediction of the next target position based on the target’s previous behavior while increasing the tracking view and lighting conditions via the view from O-D IR camera. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCDS.2017.2726356 | IEEE Transactions on Cognitive and Developmental Systems |
Keywords | Field | DocType |
Target tracking,Cameras,Robot vision systems,Kalman filters,Mobile robots | Omni directional,Computer vision,Thermal,Invariant (physics),Computer science,Tracking system,Kalman filter,Frame rate,Artificial intelligence,Maximum a posteriori estimation,Mobile robot | Journal |
Volume | Issue | ISSN |
11 | 1 | 2379-8920 |
Citations | PageRank | References |
1 | 0.39 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emrah Benli | 1 | 2 | 1.76 |
| Yuichi Motai | 2 | 230 | 24.68 |
| John Rogers | 3 | 108 | 16.07 |