Object Tracking over a Multiple-Camera Network

2015 ◽  
Author(s):  
Zhenzhong Chen ◽  
Weihang Liao ◽  
Bin Xu ◽  
Hongyi Liu ◽  
Qisheng Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Camera Network ◽  
Multiple Camera

Object tracking using multiple camera video streams

2010 ◽  
Author(s):  
Mehrube Mehrubeoglu ◽  
Diego Rojas ◽  
Lifford McLauchlan
Keyword(s):  
Object Tracking ◽  
Video Streams ◽  
Multiple Camera

scholarly journals Automatic Multi-Camera Extrinsic Parameter Calibration Based on Pedestrian Torsors †

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4989
Author(s):  
Truong ◽  
Philips ◽  
Deligiannis ◽  
Abrahamyan ◽  
Guan
Keyword(s):  
Brute Force ◽  
Camera Network ◽  
Extrinsic Calibration ◽  
Multiple Camera ◽  
Extrinsic Parameter ◽  
Extrinsic Parameters ◽  
Brute Force Method ◽  
Controlled Environments ◽  
Novel Method ◽  
Fully Automatic

Extrinsic camera calibration is essential for any computer vision task in a camera network. Typically, researchers place a calibration object in the scene to calibrate all the cameras in a camera network. However, when installing cameras in the field, this approach can be costly and impractical, especially when recalibration is needed. This paper proposes a novel, accurate and fully automatic extrinsic calibration framework for camera networks with partially overlapping views. The proposed method considers the pedestrians in the observed scene as the calibration objects and analyzes the pedestrian tracks to obtain extrinsic parameters. Compared to the state of the art, the new method is fully automatic and robust in various environments. Our method detect human poses in the camera images and then models walking persons as vertical sticks. We apply a brute-force method to determines the correspondence between persons in multiple camera images. This information along with 3D estimated locations of the top and the bottom of the pedestrians are then used to compute the extrinsic calibration matrices. We also propose a novel method to calibrate the camera network by only using the top and centerline of the person when the bottom of the person is not available in heavily occluded scenes. We verified the robustness of the method in different camera setups and for both single and multiple walking people. The results show that the triangulation error of a few centimeters can be obtained. Typically, it requires less than one minute of observing the walking people to reach this accuracy in controlled environments. It also just takes a few minutes to collect enough data for the calibration in uncontrolled environments. Our proposed method can perform well in various situations such as multi-person, occlusions, or even at real intersections on the street.

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