Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery

Gade, Rikke; Jorgensen, A. M.; Moeslund, Thomas B.

doi:10.1109/cvpr.2013.474

Cited by 102 publications

(26 citation statements)

References 40 publications

(42 reference statements)

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“…Features traditionally used in RGB images, such as histograms of oriented gradients and other invariant features, have been adapted to IR images for person detection [14]. Recently, [9] combined background modeling in infrared with grouping analysis to perform long-term occupancy analysis.…”

Section: Previous Workmentioning

confidence: 99%

“…Using expensive IR cameras, tracking can be done using only thermal IR imagery [5,19,9]. In this paper, however, we consider what can be done with a very low-cost thermal infrared sensor, whose low resolution and extremely low frame rate preclude the possibility of tracking using IR information alone.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we will focus on person tracking in indoor scenes, in which in addition to people, there can be many heated inanimate objects such as computers, monitors and TV screens, hot drinks, and room heaters. Given the low spatial, temporal, and thermal resolution of our low-cost IR sensor, as well as variation in the temperature profile of a person due to clothing, we cannot simply use background subtraction in IR images as in [9] to determine the locations of people. Figure 1 shows an example image from our IR sensor, along with a corresponding image from the RGB camera.…”

Section: Introductionmentioning

confidence: 99%

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Improving Person Tracking Using an Inexpensive Thermal Infrared Sensor

Kumar

Marks

Jones

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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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. CVPR 2014 WorkshopsThis work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. AbstractThis 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 pixelto-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 reasonin...

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Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Person Tracking Using an Inexpensive Thermal Infrared Sensor

Kumar

Marks

Jones

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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“…The counting algorithm is thoroughly evaluated in [29], but we compare here the ground truth number of targets with the estimated number for each test sequence. Figure 6 presents the results of the counting algorithm for all test sequences.…”

Section: Countingmentioning

confidence: 99%

“…An estimation of this occupancy pattern can be calculated using the approach presented in [29], which will be described briefly in the remaining part of this section.…”

Section: Counting Peoplementioning

confidence: 99%

Constrained multi-target tracking for team sports activities

Gade

Moeslund

2018

IPSJ T Comput Vis Appl

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In sports analysis, player tracking is essential to the extraction of statistics such as speed, distance and direction of motion. Simultaneous tracking of multiple people is still a very challenging computer vision problem to which there is no satisfactory solution. This is especially true for sports activities, for which people often wear similar uniforms, move quickly and erratically, and have close interactions with each other. In this paper, we introduce a multi-target tracking algorithm suitable for team sports activities. We extend an existing algorithm by including an automatic estimation of the occupancy of the observed field and the duration of stable periods without people entering or leaving the field. This information is included as a constraint to the existing offline tracking algorithm in order to construct more reliable trajectories. On data from two challenging sports scenarios-an indoor soccer game captured with thermal cameras and an outdoor soccer training session captured with RGB camera-we show that the tracking performance is improved on all sequences. Compared to the original offline tracking algorithm, we obtain improvements of 3-7% in accuracy. Furthermore, the method outperforms two state-of-the-art trackers.

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Classification of Sports Types Using Thermal Imagery

Gade

Moeslund

2014

Computer Vision in Sports

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Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery

Cited by 102 publications

References 40 publications

Improving Person Tracking Using an Inexpensive Thermal Infrared Sensor

Improving Person Tracking Using an Inexpensive Thermal Infrared Sensor

Constrained multi-target tracking for team sports activities

Classification of Sports Types Using Thermal Imagery

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