Local Feature Based Person Detection and Tracking Beyond the Visible Spectrum

Jungling, Kai; Arens, Michael

doi:10.1007/978-3-642-11568-4_1

Cited by 7 publications

(6 citation statements)

References 31 publications

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“…7-8 saturated and street are recorded in the German test village of Bonnland by the Fraunhofer IOSB institute [11]. The background is urban and the objects to track are humans.…”

Section: Included Sequencesmentioning

confidence: 99%

A thermal Object Tracking benchmark

Berg¹,

Ahlberg²,

Felsberg

2015

2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Short-term single-object (STSO) tracking in thermal images is a challenging problem relevant in a growing number of applications. In order to evaluate STSO tracking algorithms on visual imagery, there are de facto standard benchmarks. However, we argue that tracking in thermal imagery is different than in visual imagery, and that a separate benchmark is needed. The available thermal infrared datasets are few and the existing ones are not challenging for modern tracking algorithms. Therefore, we hereby propose a thermal infrared benchmark according to the Visual Visual Object Tracking (VOT) protocol for evaluation of STSO tracking methods. The benchmark includes the new LTIR dataset containing 20 thermal image sequences which have been collected from multiple sources and annotated in the format used in the VOT Challenge. In addition, we show that the ranking of different tracking principles differ between the visual and thermal benchmarks, confirming the need for the new benchmark.

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“…7-8 saturated and street are recorded in the German test village of Bonnland by the Fraunhofer IOSB institute [11]. The background is urban and the objects to track are humans.…”

Section: Included Sequencesmentioning

confidence: 99%

A thermal Object Tracking benchmark

Berg¹,

Ahlberg²,

Felsberg

2015

2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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“…In [8], thermal cameras have been applied to track pedestrians from an upfront viewpoint for night driving using a fusion of the hyper permutation network, a hierarchical contour matching algorithm and a cascaded classifier, respectively. A detector based on SURF features aided by a Kalman filter to predict the motion of individual features has been proposed in [9].…”

Section: B From Visible-light To Thermal Image Sequencesmentioning

confidence: 99%

People detection and tracking from aerial thermal views

Portmann

Lynen

Chli³

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

116

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Abstract-Detection and tracking of people in visible-light images has been subject to extensive research in the past decades with applications ranging from surveillance to searchand-rescue. Following the growing availability of thermal cameras and the distinctive thermal signature of humans, research effort has been focusing on developing people detection and tracking methodologies applicable to this sensing modality. However, a plethora of challenges arise on the transition from visible-light to thermal images, especially with the recent trend of employing thermal cameras onboard aerial platforms (e.g. in search-and-rescue research) capturing oblique views of the scenery. This paper presents a new, publicly available dataset of annotated thermal image sequences, posing a multitude of challenges for people detection and tracking. Moreover, we propose a new particle filter based framework for tracking people in aerial thermal images. Finally, we evaluate the performance of this pipeline on our dataset, incorporating a selection of relevant, state-of-the-art methods and present a comprehensive discussion of the merits spawning from our study.

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“…The second way is the 3D scanning like the time of flight that uses both the distance and time to calculate the point in 3D and the structured light that uses a pattern of projected light to calculate the depth image [4].…”

Section: Introductionmentioning

confidence: 99%

Kinect-Based Moving Human Tracking System with Obstacle Avoidance

Ahmad¹,

Bazzal²,

Youssef³

2017

Adv. sci. technol. eng. syst. j.

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This paper is an extension of work originally presented and published in IEEE International Multidisciplinary Conference on Engineering Technology (IMCET). This work presents a design and implementation of a moving human tracking system with obstacle avoidance. The system scans the environment by using Kinect, a 3D sensor, and tracks the center of mass of a specific user by using Processing, an open source computer programming language. An Arduino microcontroller is used to drive motors enabling it to move towards the tracked user and avoid obstacles hampering the trajectory. The implemented system is tested under different lighting conditions and the performance is analyzed using several generated depth images.

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Local Feature Based Person Detection and Tracking Beyond the Visible Spectrum

Cited by 7 publications

References 31 publications

A thermal Object Tracking benchmark

A thermal Object Tracking benchmark

People detection and tracking from aerial thermal views

Kinect-Based Moving Human Tracking System with Obstacle Avoidance

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