A New Outdoor Object Tracking Approach in Video Surveillance

Kim, SoonWhan; Kang, Jin-Shig

doi:10.1007/978-3-319-05500-8_16

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…In Ref. 34, the size of the window is set as three or four times of the size of the previously detected object. In this work, the size of the ROI is determined by the predicted error covariance matrix provided by the filter, as it indicates the uncertainty of the prediction.…”

Section: Kalman Filter For Light-emitting Diode Trackingmentioning

confidence: 99%

Handheld pose tracking using vision-inertial sensors with occlusion handling

Slembrouck

Deboeverie

et al. 2016

J. Electron. Imaging

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Abstract. Tracking of a handheld device's three-dimensional (3-D) position and orientation is fundamental to various application domains, including augmented reality (AR), virtual reality, and interaction in smart spaces. Existing systems still offer limited performance in terms of accuracy, robustness, computational cost, and ease of deployment. We present a low-cost, accurate, and robust system for handheld pose tracking using fused vision and inertial data. The integration of measurements from embedded accelerometers reduces the number of unknown parameters in the six-degree-of-freedom pose calculation. The proposed system requires two light-emitting diode (LED) markers to be attached to the device, which are tracked by external cameras through a robust algorithm against illumination changes. Three data fusion methods have been proposed, including the triangulation-based stereo-vision system, constraint-based stereo-vision system with occlusion handling, and triangulation-based multivision system. Real-time demonstrations of the proposed system applied to AR and 3-D gaming are also included. The accuracy assessment of the proposed system is carried out by comparing with the data generated by the state-of-the-art commercial motion tracking system OptiTrack. Experimental results show that the proposed system has achieved high accuracy of few centimeters in position estimation and few degrees in orientation estimation.

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Section: Kalman Filter For Light-emitting Diode Trackingmentioning

confidence: 99%

Handheld pose tracking using vision-inertial sensors with occlusion handling

Slembrouck

Deboeverie

et al. 2016

J. Electron. Imaging

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show abstract

“…2.1 Summary of Some Definitions Proposed by in [18,19] Several parameters such as object window, object area, and expansion and contraction parameter defined in are reintroduced in this paper. The binary image is denoted by I, and I x and I y are defined as…”

Section: Problem Formulation and Definitionsmentioning

confidence: 99%

A Modified Expansion-Contraction Method for Mobile Object Tracking in Video Surveillance: Indoor Environment

Kang

2013

International Journal of Fuzzy Logic and Intelligent Systems

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Recent years have witnessed a growing interest in the fields of video surveillance and mobile object tracking. This paper proposes a mobile object tracking algorithm. First, several parameters such as object window, object area, and expansion-contraction (E-C) parameter are defined. Then, a modified E-C algorithm for multiple-object tracking is presented. The proposed algorithm tracks moving objects by expansion and contraction of the object window. In addition, it includes methods for updating the background image and avoiding occlusion of the target image. The validity of the proposed algorithm is verified experimentally. For example, the first scenario traces the path of two people walking in opposite directions in a hallway, whereas the second one is conducted to track three people in a group of four walkers.

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“…For example, a fixed size of 32×32 pixels is chosen in Kwon, Shin, and Paik (2006). In Kim and Kang (2014), the size of the window is set as three or four times of the size of the previously detected object.…”

Section: Generating the Region Of Interest (Roi) Using Predictionmentioning

confidence: 99%

A new efficient pose estimation and tracking method for personal devices : application to interaction in smart spaces

Li¹

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A New Outdoor Object Tracking Approach in Video Surveillance

Cited by 4 publications

References 8 publications

Handheld pose tracking using vision-inertial sensors with occlusion handling

Handheld pose tracking using vision-inertial sensors with occlusion handling

A Modified Expansion-Contraction Method for Mobile Object Tracking in Video Surveillance: Indoor Environment

A new efficient pose estimation and tracking method for personal devices : application to interaction in smart spaces

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