Learning flexible models from image sequences

Baumberg, Adam; Hogg, David C.

doi:10.1007/3-540-57956-7_34

Cited by 170 publications

(125 citation statements)

References 7 publications

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“…Image-based tracking schemes that emphasize learning of views or motion have focused on region contours (Baumberg and Hogg, 1994;Blake et al, 1994;Cootes et al, 1992;Kervrann and Heitz, 1994). In particular, Baumberg and Hogg (1994) track articulated objects by first computing a silhouette of the object via image differencing.…”

Section: Related Workmentioning

confidence: 99%

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EigenTracking: Robust matching and tracking of articulated objects using a view-based representation

Black

Jepson

1996

Lecture Notes in Computer Science

579

812

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Abstract. This paper describes an approach for tracking rigid and articulated objects using a view-based representation. The approach builds on and extends work on eigenspace representations, robust estimation techniques, and parameterized optical flow estimation. First, we note that the least-squares image reconstruction of standard eigenspace techniques has a number of problems and we reformulate the reconstruction problem as one of robust estimation. Second we define a "subspace constancy assumption" that allows us to exploit techniques for parameterized optical flow estimation to solve for both the view of an object and the affine transformation between the eigenspace and the image. To account for large affine transformations between the eigenspace and the image we define a multi-scale eigenspace representation and a coarse-to-fine matching strategy. Finally, we use these techniques to track objects over long image sequences in which the objects simultaneously undergo both affine image motions and changes of view. In particular we use this "EigenTracking" technique to track and recognize the gestures of a moving hand.

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Section: Related Workmentioning

confidence: 99%

“…In particular, Baumberg and Hogg (1994) track articulated objects by first computing a silhouette of the object via image differencing. They fit a spline to the object's outline and the knot points of the spline form the representation of the current view.…”

Section: Related Workmentioning

confidence: 99%

EigenTracking: Robust matching and tracking of articulated objects using a view-based representation

Black

Jepson

1996

Lecture Notes in Computer Science

579

812

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“…One approach to the detection and tracking problem is to fit explicit object models of shape, such as rigid wireframe CAD models [15,16] or flexible active shape models [17]. Some model fitting approaches focused on high-level reasoning [18,19].…”

Section: Previous Workmentioning

confidence: 99%

Object Modeling for Multicamera Correspondence Using Fuzzy Region Color Adjacency Graphs

Khalili

Kasaei

2008

Communications in Computer and Information Science

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In this paper, a novel moving object modeling suitable for multicamera correspondence is introduced. Taking into consideration the color and motion features of foreground objects in each independent video stream, our method segments the existing moving objects and constructs a graph-based structure to maintain the relational information of each segment. Using such graph structures reduces our correspondence problem to a subgraph optimal isomorphism problem. The proposed method is robust against various resolutions and orientations of objects at each view. Our system uses the fuzzy logic to employ a human-like color perception in its decision making stage in order to handle color inconstancy which is a common problem in multiview systems. The computational cost of the proposed method is made low to be applied in real-time applications. Also, it can solve the partial occlusion problem more precisely than the Meanshif occlusion solver by 15.7%.

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“…First, a statistical shape model of a pedestrian was built using automatically segmented pedestrian contours from sequences obtained by a stationary camera (so that we can do background subtraction). We use well-established computer vision techniques (see [22] and [23]) to build a LPDM (Linear Point Distribution Model). We fit a NURB (Non-Uniform Rational B-spline) to each extracted contour using least squares curve approximation to points on the contour [21].…”

Section: Tracking Pedestrians From a Moving Vehiclementioning

confidence: 99%

Quasi-Random Sampling for Condensation

Philomin

Duraiswami

Davis

2000

Lecture Notes in Computer Science

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Abstract. The problem of tracking pedestrians from a moving car is a challenging one. The Condensation tracking algorithm is appealing for its generality and potential for real-time implementation. However, the conventional Condensation tracker is known to have di culty with high-dimensional state spaces and unknown motion models. This paper presents an improved algorithm that addresses these problems by using a simplified motion model, and employing quasi-Monte Carlo techniques to e ciently sample the resulting tracking problem in the high-dimensional state space. For N sample points, these techniques achieve sampling errors of O(N 31 ), as opposed to O(N 31/2 ) for conventional Monte Carlo techniques. We illustrate the algorithm by tracking objects in both synthetic and real sequences, and show that it achieves reliable tracking and significant speed-ups over conventional Monte Carlo techniques.

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Learning flexible models from image sequences

Cited by 170 publications

References 7 publications

EigenTracking: Robust matching and tracking of articulated objects using a view-based representation

EigenTracking: Robust matching and tracking of articulated objects using a view-based representation

Object Modeling for Multicamera Correspondence Using Fuzzy Region Color Adjacency Graphs

Quasi-Random Sampling for Condensation

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