TR2014-036
Improving Person Tracking Using an Inexpensive Thermal Infrared Sensor
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- "Improving Person Tracking Using an Inexpensive Thermal Infrared Sensor", IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), DOI: 10.1109/CVPRW.2014.41, June 2014, pp. 217-224.BibTeX TR2014-036 PDF
- @inproceedings{Kumar2014jun,
- author = {Kumar, S. and Marks, T.K. and Jones, M.},
- title = {Improving Person Tracking Using an Inexpensive Thermal Infrared Sensor},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
- year = 2014,
- pages = {217--224},
- month = jun,
- publisher = {IEEE},
- doi = {10.1109/CVPRW.2014.41},
- url = {https://www.merl.com/publications/TR2014-036}
- }
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- "Improving Person Tracking Using an Inexpensive Thermal Infrared Sensor", IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), DOI: 10.1109/CVPRW.2014.41, June 2014, pp. 217-224.
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Abstract:
This paper proposes a person tracking framework using a scanning low-resolution thermal infrared (IR) sensor colocated with a wide-angle RGB camera. The low temporal and spatial resolution of the low-cost IR sensor make it unable to track moving people and prone to false detections of stationary people. Thus, IR-only tracking using only this sensor would be quite problematic. We demonstrate that despite the limited capabilities of this low-cost IR sensor, it can be used effectively to correct the errors of a real-time RGB camera-based tracker. We align the signals from the two sensors both spatially (by computing a pixel-to-pixel geometric correspondence between the two modalities) and temporally (by modeling the temporal dynamics of the scanning IR sensor), which enables multi-modal improvements based on judicious application of elementary reasoning. Our combined RGB+IR system improves upon the RGB camera-only tracking by: rejecting false positives, improving segmentation of tracked objects, and correcting false negatives (starting new tracks for people that were missed by the camera-only tracker). Since we combine RGB and thermal information at the level of RGB camera-based tracks, our method is not limited to the particular camera-based tracker that we used in our experiments. Our method could improve the results of any tracker that uses RGB camera input alone. We collect a new dataset and demonstrate the superiority of our method over RGB camera-only tracking.