Pedestrian tracking with an infrared sensor using road network information

Skoglar, Per; Orguner, Umut; Törnqvist, David; Gustafsson, Fredrik

doi:10.1186/1687-6180-2012-26

Cited by 8 publications

(8 citation statements)

References 43 publications

(60 reference statements)

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“…[165] uses a multiplemodel particle filter, and prior information about the walkways to enhance the performance. [178] does also use a particle filter, combined with two shape-and feature-based measurement models, to track humans in real time from a mobile robot.…”

Section: Detection and Tracking Of Humansmentioning

confidence: 99%

Thermal cameras and applications: a survey

Gade

Moeslund

2013

Machine Vision and Applications

610

269

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Thermal cameras are passive sensors that capture the infrared radiation emitted by all objects with a temperature above absolute zero. This type of camera was originally developed as a surveillance and night vision tool for the military, but recently the price has dropped, significantly opening up a broader field of applications. Deploying this type of sensor in vision systems eliminates the illumination problems of normal greyscale and RGB cameras.This survey provides an overview of the current applications of thermal cameras. Applications include animals, agriculture, buildings, gas detection, industrial, and military applications, as well as detection, tracking, and recognition of humans. Moreover, this survey describes the nature of thermal radiation and the technology of thermal cameras.

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Section: Detection and Tracking Of Humansmentioning

confidence: 99%

Thermal cameras and applications: a survey

Gade

Moeslund

2013

Machine Vision and Applications

610

269

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“…Goubet et al [11] also rely on high contrast between object and background. In contrast, Skoglar et al [27] use a pre-trained boosting based detector and improve tracking in a surveillance application using road network information and a multimodal PF. Portmann et al [24] combine background subtraction and a part-based detector using a support vector machine to classify Histograms of Gradients.…”

Section: Related Workmentioning

confidence: 99%

“…Many published methods on TIR tracking have strong constraints, for example assuming static camera and/or background [10,11,21], pre-trained detector (implying known object class) [15,24,27], and high objectbackground contrast [10,11,21].…”

Section: Related Workmentioning

confidence: 99%

Channel Coded Distribution Field Tracking for Thermal Infrared Imagery

Berg

Ahlberg

Felsberg

2016

2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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We address short-term, single-object tracking, a topic that is currently seeing fast progress for visual video, for the case of thermal infrared (TIR) imagery. The fast progress has been possible thanks to the development of new template-based tracking methods with online template updates, methods which have not been explored for TIR tracking. Instead, tracking methods used for TIR are often subject to a number of constraints, e.g., warm objects, low spatial resolution, and static camera. As TIR cameras become less noisy and get higher resolution these constraints are less relevant, and for emerging civilian applications, e.g., surveillance and automotive safety, new tracking methods are needed.Due to the special characteristics of TIR imagery, we argue that template-based trackers based on distribution fields should have an advantage over trackers based on spatial structure features. In this paper, we propose a templatebased tracking method (ABCD) designed specifically for TIR and not being restricted by any of the constraints above. In order to avoid background contamination of the object template, we propose to exploit background information for the online template update and to adaptively select the object region used for tracking. Moreover, we propose a novel method for estimating object scale change. The proposed tracker is evaluated on the VOT-TIR2015 and VOT2015 datasets using the VOT evaluation toolkit and a comparison of relative ranking of all common participating trackers in the challenges is provided. Further, the proposed tracker, ABCD, and the VOT-TIR2015 winner SRDCFir are evaluated on maritime data. Experimental results show that the ABCD tracker performs particularly well on thermal infrared sequences.

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“…Apart from the mode parameter, the longitudinal distance x r t+1 must also be updated. See Skoglar et al [44] for a more detailed description of the road target model. It is also quite straightforward to use alternative longitudinal motion models if a different target behavior is desired, for instance see [56] for a suitable second order linear Gaussian model.…”

Section: Appendixmentioning

confidence: 99%

“…Since the particle filters can handle nonlinear and non-Gaussian models, the user has much more freedom than in Kalman filter and IMM modeling. In this work the road target tracking approach in [44] is used, but the association problem is ignored by assuming good discrimination among the targets.…”

Section: Background and Literature Surveymentioning

confidence: 99%

Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle

et al. 2012

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This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids.

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Pedestrian tracking with an infrared sensor using road network information

Cited by 8 publications

References 43 publications

Thermal cameras and applications: a survey

Thermal cameras and applications: a survey

Channel Coded Distribution Field Tracking for Thermal Infrared Imagery

Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle

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