Download Computer Vision — ECCV 2002: 7th European Conference on by Arthur E. C. Pece, Anthony D. Worrall (auth.), Anders PDF

By Arthur E. C. Pece, Anthony D. Worrall (auth.), Anders Heyden, Gunnar Sparr, Mads Nielsen, Peter Johansen (eds.)

The 4 quantity set LNCS 2350/2351/2352/2353 constitutes the refereed court cases of the seventh eu convention on desktop imaginative and prescient, ECCV 2002, held in Copenhagen, Denmark, in may possibly 2002. The 226 revised complete papers awarded have been conscientiously reviewed and chosen from a complete of round six hundred submissions. The 4 books supply topical sections on energetic and real-time imaginative and prescient, photograph positive factors, visible movement, floor geometry, grouping and segmentation, stereoscopic imaginative and prescient, constitution from movement, form, item acceptance, colour and shading, imaginative and prescient platforms, statistical studying, robotic imaginative and prescient, and calibration.

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21. AD Worrall, JM Ferryman, GD Sullivan, KD Baker, Pose and structure recovery using active models. Proc. 137-146, 1995. 22. AL Yuille, JM Coughlan, Fundamental limits of Bayesian inference: order parameters and phase transitions for road tracking. IEEE Trans. PAMI 22(2): 160-173, 2000. M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene Using Region-Based Stereo Anurag Mittal and Larry S. edu Abstract. We present a system that is capable of segmenting, detecting and tracking multiple people in a cluttered scene using multiple synchronized cameras located far from each other.

Rubin, Maximum likelihood from incomplete data via the EM algorithm (with discussion), J. of the Royal Statistical Soc. B 39: 1-38, 1977. Tracking with the EM Contour Algorithm 17 5. JM Ferryman, AD Worrall, Proc. of the 1st IEEE International Workshop on Performance Evaluation in Tracking and Surveillance: PETS 2000. 6. D Geman, B Jedynak, An active testing model for tracking roads in satellite images. IEEE Trans. PAMI 18(1): 1-14, 1996. 7. C Harris, Tracking with rigid models. 59-73. MIT Press, 1992.

Tracking with the EM Contour Algorithm 13 In addition to Eq. 29, a dynamical model is needed to predict the state of the object at the next frame. The form of the model is as follows: x(t + ∆t) = D [x(t)] + z(t + ∆t) (30) where ∆t is the time interval between video frames (or, in the case of an iterated Kalman filter, between iteration steps), D describes the deterministic dynamics of the system and z is a random vector which includes unknown control inputs from the driver. Simple physics leads to the following dynamical equations: – position on the ground plane from orientation and tangential velocity: X(t + ∆t) = X(t) + v(t)∆t cos θZ (t) Y (t + ∆t) = Y (t) + v(t)∆t sin θZ (t) (31) – orientation and tangential velocity from angular velocity and tangential acceleration: θZ (t + ∆t) = θZ (t) + ωZ (t)∆t v(t + ∆t) = v(t) + a(t)∆t (32) – angular velocity and tangential acceleration from the driver’s input: ωZ (t + ∆t) = ωZ (t)[1 − exp(−∆t/τ )] + z5 (t + ∆t) a(t + ∆t) = a(t)[1 − exp(−∆t/τ )] + z6 (t + ∆t) (33) The simplifying assumption is that changes of pressure on the gas pedal and of steering angle directly translate into tangential acceleration and angular velocity.

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