Multiple-view-based tracking of multiple humans

Utsumi, Akira; Mori, Hiroki; Ohya, Jun; Yachida, Masahiko

doi:10.1109/icpr.1998.711214

Cited by 30 publications

(18 citation statements)

References 10 publications

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“…Some systems have access to more cameras than required and therefore need to decide which camera(s) or image(s) to use at each time instant. Various measurements are introduced to quantify the individual viewpoints in terms of ambiguity [112], occlusion [75], and in general reliability of data [67,137].…”

Section: Tracking Over Timementioning

confidence: 99%

A Survey of Computer Vision-Based Human Motion Capture

Moeslund¹,

Granum²

2001

Computer Vision and Image Understanding

1,422

642

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Section: Tracking Over Timementioning

confidence: 99%

A Survey of Computer Vision-Based Human Motion Capture

Moeslund¹,

Granum²

2001

Computer Vision and Image Understanding

1,422

642

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“…A relationship between camera views using homography is considered in [7]. To resolve mutual occlusion the best view is utilized in [8]. A similar method that based on a fusion of information from every camera into a 3-D geometric coordinate system is presented in the work [9].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Fusion for Multiple Camera Tracking

Amato

Haj

Mozerov

et al. 2007

Advances in Soft Computing

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Summary. In this paper we present a robust and efficient method to overcome the negative effects of occlusion in the tracking process of multiple agents. The proposed approach is based on the matching of multiple trajectories from multiple views using spatial and temporal information. These trajectories are represented as consecutive points of a joint ground plane in the world coordinate system that belong to the same tracked agent. We introduce an integral distance between compared trajectories, which allows us to avoid mismatches, due to the possible measurement outliers in one frame. The proposed method can also be considered as an interpolation algorithm of a disconnected trajectory during the time of occlusion. This technique solves one of the most difficult problems of occlusion handling, which is a matching of two unconnected parts of the same trajectory.

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“…This is because by using multiple cameras the area of surveillance is expanded and information from multiple views is extremely helpful to handle many issues such as occlusion etc. However, visual surveillance using multiple cameras also brings a number of problems such as camera installation [26], calibration of multiple cameras [13,27], correspondence between multiple cameras [1 -11], automated camera switching [9], and data fusion [28][29][30][31], etc. In this paper, we focus on correspondence between multiple cameras.…”

Section: Introductionmentioning

confidence: 99%

Principal axis-based correspondence between multiple cameras for people tracking

Zhou

et al. 2006

IEEE Trans. Pattern Anal. Machine Intell.

209

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Visual surveillance using multiple cameras has attracted increasing interest in recent years. Correspondence between multiple cameras is one of the most important and basic problems which visual surveillance using multiple cameras brings. In this paper, we propose a simple and robust method, based on principal axes of people, to match people across multiple cameras. The correspondence likelihood reflecting the similarity of principal axis pairs is constructed according to the relationship between "ground-points" detected in each camera view and the intersections of principal axes detected in different camera views and transformed to the same view. Our method has the following desirable properties: (1) Camera calibration is not needed. (2) Accurate motion detection and segmentation are less critical due to the robustness of the principal axis-based feature to noise. (3) Based on the fused data derived from correspondence results, positions of people in each camera view can be accurately located even when the people are partially occluded in all views. The experimental results on several real video sequences from outdoor environments have demonstrated the effectiveness, efficiency and robustness of our method.

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Multiple-view-based tracking of multiple humans

Cited by 30 publications

References 10 publications

A Survey of Computer Vision-Based Human Motion Capture

A Survey of Computer Vision-Based Human Motion Capture

Trajectory Fusion for Multiple Camera Tracking

Principal axis-based correspondence between multiple cameras for people tracking

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