S. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler. Tracking groups of people. Computer Vision and Image Understanding, 80(1):42--56, October 2000. Home/Search Document Not in Database Summary Related Articles Check |
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A Real-Time System for Classification of Moving Objects - Rivlin, Rudzsky.. (2002) (Correct)
....best matches the object attributes stored in the database. Figure 2. a) Moving object bounding box (b) The color map An additional processing includes finding and uniting detached object parts using the stored target template and removing shadows by geometric and color based filters [3] 7] [11]. The object entry in the database is updated with the newly found target data in the current frame. The object is removed from the database if it is not detected for a number of frames or when it leaves the field of view. The mutual occlusion by the moving objects is predicted by analyzing the ....
S. J. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler. Tracking groups of people. Computer Vision and Image Understanding, 80:42--56, 2000.
Efficient Non-parametric Adaptive Color Modeling Using.. - Elgammal, Duraiswami.. (2001) (Correct)
....number of Gaussians for the assumed model (model selection) Nonparametric techniques using histograms have been widely used for modeling the color of object for different applications to overcome the previously mentioned problems with parametric models. Color histograms have been used in [12] for people tracking. This work used 3 dimensional adaptive histograms in RGB space to model the color of the whole person. Color histograms have also been used in [11] for tracking hands, in [2] for color region tracking and in [10] for skin detection. The major drawback with color ....
S. J. McKenna, S. Jabri, Z. Duric, and A. Rosenfeld. Tracking groups of people. Computer Vision and Image Understanding, (80):42--56, 2000.
Probabilistic Framework for Segmenting People Under Occlusion - Elgammal, Davis (2001) (10 citations) (Correct)
....system is able to count the number of people in the groups as long as their heads appear as part of the outer silhouette of the group; it would fail otherwise. The Hydra system was not intended to accurately segment the group into individuals nor does it recover depth information. McKenna et al. [6] segment groups of people based on the individuals color distribution. They represent the color distribution of the whole person by a histogram and use this to segment the group. The color features are represented globally and are not spatially localized; therefore their approach loses the spatial ....
.... that object motion is constrained to the ground plane is valid for people and cars but would fail if the contact point on the ground plane is not visible because of partial occlusion by other objects, or because contact points are out of the field of view (for example, see figure 1) McKenna et al. [6] define the visibility index to be the ratio between the number of pixel visible of each person during occlusion to the expected number of pixels for that person when isolated. They use this visibility index to measure the depth (higher visibility index indicates that the person is in front) ....
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S. J. McKenna, S. Jabri, Z. Duric, and A. Rosenfeld. Tracking groups of people. Computer Vision and Image Understanding, (80):42--56, 2000.
Efficient Non-parametric Adaptive Color Modeling Using.. - Elgammal, Duraiswami.. (2001) (Correct)
....right number of Gaussians for the assumed model (model selection) Nonparametric techniques using histograms have been widely used for modeling the color of object for different applications to overcome the previously mentioned problems with parametric models. Color histograms have been used in [12] for people tracking. This work used 3 dimensional adaptive histograms in RGB space to model the color of the whole person. Color histograms have also been used in [11] for tracking hands, in [2] for color region tracking and in [10] for skin detection. The major drawback with color histograms is ....
S. J. McKenna, S. Jabri, Z. Duric, and A. Rosenfeld. Tracking groups of people. Computer Vision and Image Understanding, (80):42--56, 2000.
Probabilistic Framework for Segmenting People Under Occlusion - Elgammal, Davis (2001) (10 citations) (Correct)
....system is able to count the number of people in the groups as long as their heads appear as part of the outer silhouette of the group; it would fail otherwise. The Hydra system was not intended to accurately segment the group into individualsnor does it recover depth information. McKenna et al. [6] segment groups of people based on the individuals color distribution. They represent the color distribution of the whole person by a histogram and use this to segment the group. The color features are represented globally and are not spatially localized; therefore their approach loses the spatial ....
.... that object motion is constrained to the ground plane is valid for people and cars but would fail if the contact point on the ground plane is not visible because of partial occlusion by other objects, or because contact points are out of the field of view (for example, see figure 1) McKenna et al. [6] define the visibility index to be the ratio between the number of pixel visible of each person during occlusion to the expected number of pixels for that person when isolated. They use this visibility index to measure the depth (higher visibility index indicates that the person is in front) ....
[Article contains additional citation context not shown here]
S. J. McKenna, S. Jabri, Z. Duric, and A. Rosenfeld. Tracking groups of people. Computer Vision and Image Understanding, (80):42--56, 2000.
Online Appearance Learning for 3D Articulated Human Tracking - Timothy Roberts Stephen (2002) Self-citation (Mckenna) (Correct)
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S. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler. Tracking groups of people. Computer Vision and Image Understanding, 80(1):42--56, October 2000.
Human Tracking Using 3D Surface Colour Distributions - Roberts, McKenna, Ricketts (2003) Self-citation (Mckenna) (Correct)
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S. J. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, H. Wechsler, Tracking groups of people, Computer Vision and Image Understanding 80 (1) (2000) 42--56. 21
Adaptive Learning of Statistical Appearance Models for.. - Roberts, McKenna.. (2002) (1 citation) Self-citation (Mckenna) (Correct)
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S. J. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler. Tracking groups of people. Computer Vision and Image Understanding, 80(1):42--56, October 2000.
Adaptive Learning of Statistical Appearance Models for.. - Roberts, McKenna.. (2002) (1 citation) Self-citation (Mckenna) (Correct)
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S. J. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler. Tracking groups of people. Computer Vision and Image Understanding, 80(1):42--56, October 2000.
Online Appearance Learning for 3D Articulated Human Tracking - Roberts, McKenna, Ricketts (2002) Self-citation (Mckenna) (Correct)
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S. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler. Tracking groups of people. Computer Vision and Image Understanding, 80(1):42--56, October 2000.
Adaptive Learning of Statistical Appearance Models for.. - Roberts, McKenna.. (2002) (1 citation) Self-citation (Mckenna) (Correct)
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S. J. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler. Tracking groups of people. Computer Vision and Image Understanding, 80(1):42--56, October 2000.
Activity Summarisation and Fall Detection in a Supportive .. - Hammadi Nait-Charif And (2004) Self-citation (Mckenna) (Correct)
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S. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler. Tracking groups of people. Computer Vision and Image Understanding, 80(1):42--56, 2000.
An Appearance Based Approach To - Human And Object (Correct)
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S. J. McKenna, S. Jabri, Z. Duric, A. Rosenfeld, and H. Wechsler. Tracking groups of people. Computer Vision and Image Understanding 2000, 80(1): 42-56.
Automatic event detection in CCTV surveillance Luis M. Fuentes - And Sergio Velastin (Correct)
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McKenna, S., Jabri, S., Duric, Z., Rosenfeld, A., Wechsler, H.: Tracking Groups of People. Computer Vision and Image Understanding 80 (2000) 42--56
Tracking of Human Movements in Image Space - Fabio Remondino Table (Correct)
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McKenna S., 2000: Tracking groups of people. Computer Vision and Image Understanding, vol.80, nr.1, October, pp.42-56.
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