P. J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. W. Bowyer. The gait identification challenge problem: Data sets and baseline algorithm. In Intl. Conf. on Pattern Recognition, 2002. Home/Search Document Not in Database Summary Related Articles Check |
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A Hidden Markov Model Based Framework For Recognition Of Humans - From Gait Sequences (Correct)
....be useful to have a framework that does not depend on the particular feature vector used. In the experiments conducted we have used video sequences from the USF database and selected the binary image obtained from the background subtraction algorithm provided in the database as the feature vector [9]. We compare the performance of our algorithm to the baseline algorithm proposed in [10] 2. OVERVIEW OF THE HMM FRAMEWORK Let the database consists of video sequences of persons. The model for the person is given by with number of states. The model, is ....
....scaled and aligned to the center of the frame as in Figure 1 which features part of a sequence of feature vectors. We describe in this section the methods used to obtain initial estimates of the HMM parameters, the training algorithm and finally, identification results using USF data described in [9]. 3.1. Initial Estimate of HMM Parameters In order to obtain a good estimate of the exemplars and the transition matrix, we first obtain an initial estimate of an ordered set of exemplars from the sequence and the transition matrix and successively refine the estimate. The initial estimate for ....
P.J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. Bowyer, "The gait identification challenge problem: Data sets and baseline algorithm," Proc. of the Int. Conf. on Pattern Recognition, 2002.
Towards a View Invariant Gait Recognition Algorithm - Amit Kale Amit (2003) (Correct)
....and similar for the different angles, though the upper halves of the plots become more and more noisy as the value of increases. In order to study the performance of gait recognition on the synthesized images we used a rather simple variant of the baseline gait recognition algorithm [15]. Our gallery consists of people walking at , i.e. the canonical view. The probes are video sequences where people walk at arbitrary angles . We take contiguous boxed images of a person in the gallery when he she is walking at an azimuth A Y# . For every image of the probe ....
.... G F(J F J FwwJ J . The similarity matrices, yield as a by product a quantitative assessment of the quality of the synthesized images as H Q H , K (15) for each h ; persons. This is plotted as a function in Figure (7) The cumulative match characteristics [15] are shown in Figure (9) for the full body, leg only and leg and height fusion cases. The rise of the solid curves (representing the leg dynamics, with or without height fusion) is faster 6 0 15 30 45 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Angle Quality Figure 7: Quality ....
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P. J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. W. Bowyer, "The gait identification challenge problem: Data sets and baseline algorithm," Proc of the International Conference on Pattern Recognition, 2002.
Towards a View Invariant Gait Recognition Algorithm - Kale, Chowdhury, Chellappa (2003) (Correct)
....and similar for the different angles, though the upper halves of the plots become more and more noisy as the value of increases. In order to study the performance of gait recognition on the synthesized images we used a rather simple variant of the baseline gait recognition algorithm [15]. Our gallery consists of people walking at = 0, i.e. the canonical view. The probes are video sequences where people walk at arbitrary angles . We take N contiguous boxed images of a person i in the gallery when he she is walking at an azimuth = 0. For every image of the probe j transformed ....
....max( h(j) 2 Gamma h(j) The similarity matrices, yield as a by product a quantitative assessment of the quality of the synthesized images as Q i=1 (i; i) 15) for each = x and P persons. This is plotted as a function of in Figure (7) The cumulative match characteristics [15] are shown in Figure (9) for the full body, leg only and leg and height fusion cases. The rise of the solid curves (representing the leg dynamics, with or without height fusion) is faster 6 0 15 30 45 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Angle Figure 7: Quality degradationof ....
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P. J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. W. Bowyer, "The gait identification challenge problem: Data sets and baseline algorithm," Proc of the International Conference on Pattern Recognition, 2002. 8
Video Based Rendering Of Planar Dynamic Scenes - Amit Kale Amit (Correct)
....scenes, given a video sequence of the scene at any other viewing angle. The method we propose here has many applications in vision, video processing and multimedia. However, we were motivated from the point of view of gait recognition, which forms an important aspect of human identification [1]. The gait of a person is best reflected when he she presents a side view (referred to in this paper as a canonical view) to the camera. Hence, most gait recognition algorithms rely on the availability of the side view of the subject. In realistic surveillance scenarios, however, it is ....
....the reconstructions we take the following approach. For every image synthesized from , we compute Ez y i maxcorr 1L Ez y z refers to the th image in the synthesized sequence, is the set of successive images in the canonical view and . J [j is the binary correlation [1]. The quality of the reconstruction, as a function of the azimuth angle is shown in the Figure 6. As expected, the performance degrades with increasing values of . 5. CONCLUSION In this paper, we have proposed a method for synthesizing arbitrary views of planar objects moving along a straight ....
P. J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. W. Bowyer, "The gait identification challenge problem: Data sets and baseline algorithm," Proc of the International Conference on Pattern Recognition, 2002.
SUBMITTED FOR PUBLICATION TO: IEEE TRANS. ON IMAGE.. - And Deformation.. (Correct)
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P. J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. W. Bowyer. The gait identification challenge problem: Data sets and baseline algorithm. In Intl. Conf. on Pattern Recognition, 2002.
Human Identification Using Gait and Face - Chellappa, Roy-Chowdhury, Zhou (Correct)
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P. J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. W. Bowyer. The gait identification challenge problem: Data sets and baseline algorithm. Proc of the International Conference on Pattern Recognition, 2002.
Identification of Humans Using Gait - Kale Rajagopalan Sundaresan (2004) (1 citation) (Correct)
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P. J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. W. Bowyer, "The gait identification challenge problem: Data sets and baseline algorithm," Proc of the International Conference on Pattern Recognition, 2002.
Role of Shape and Kinematics in Human Movement Analysis - Veeraraghavan, Chowdhury, al. (2004) (1 citation) (Correct)
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J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. Bowyer. The gait identification challenge problem: Data sets and baseline algorithm. ICPR, August 2002.
Recognition of Humans and Their Activities Using Video - Chellappa, Roy-Chowdhury.. (Correct)
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P. J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. W. Bowyer. The gait identification challenge problem: Data sets and baseline algorithm. Proc of the International Conference on Pattern Recognition, pages I:385--388, 2002.
Gait-Based Human Identification from a Monocular.. - Kale, Sundaresan.. (2004) (Correct)
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P. J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. W. Bowyer, "The gait identification challenge problem: Data sets and baseline algorithm," Proc of the International Conference on Pattern Recognition, 2002.
A Hidden Markov Model Based Framework For Recognition Of Humans - From Gait Sequences (Correct)
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P.J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. Bowyer, "The gait identification challenge problem: Data sets and baseline algorithm," Proc. of the Int. Conf. on Pattern Recognition, 2002.
Identification of Humans Using Gait - Kale Sundaresan Rajagopalan (2004) (1 citation) (Correct)
No context found.
P. J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. W. Bowyer, "The gait identification challenge problem: Data sets and baseline algorithm," Proc of the International Conference on Pattern Recognition, 2002.
Identification of Humans Using Gait - Kale Rajagopalan Sundaresan (2004) (1 citation) (Correct)
No context found.
P. J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. W. Bowyer, "The gait identification challenge problem: Data sets and baseline algorithm," Proc of the International Conference on Pattern Recognition, 2002.
Video Based Rendering Of Planar Dynamic Scenes - Amit Kale Amit (Correct)
No context found.
P. J. Phillips, S. Sarkar, I. Robledo, P. Grother, and K. W. Bowyer, "The gait identification challenge problem: Data sets and baseline algorithm," Proc of the International Conference on Pattern Recognition, 2002.
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