Andrew G. Backhouse

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This paper proposes a robust object tracking method in video where the time-varying principal components of object's appearance are updated online. Instead of directly updating the PCA-based subspace using matrix decomposition, the sub-space is updated by tracking on the Grassmann manifold. The object tracker performs two alternating processes: (a) online(More)
This paper addresses the issue of tracking a single visual object through crowded scenarios, where a target object may be intersected or partially occluded by other objects for a long duration, experience severe deformation and pose changes, and different motion speed in cluttered background. A robust visual object tracking scheme is proposed that exploits(More)
We propose a novel scheme that jointly employs anisotropic mean shift and particle filters for tracking moving objects from video. The proposed anisotropic mean shift, that is applied to partitioned areas in a candidate object bounding box whose parameters (center, width, height and orientation) are adjusted during the mean shift iterations, seeks multiple(More)
This paper deals with tracking of face blobs containing pose changes. We propose a novel tracking method to deal with face pose changes during the tracking. In the method, tracking is formulated as an approximate solution to the MAP estimate of the state vector, consisting of a linear and a nonlinear part. Multi-pose face appearances are described by local(More)
Channel modelling in a network path is of major importance in designing delay sensitive applications. It is often not possible for these applications to retransmit packets due to delay constraints and they must therefore be resilient to packet losses. In this paper, we first establish an association between traffic delays and the queue size at a network(More)
Designing good network-adaptive, error resilient video coders for IP networks is a challenging task. Video data packets can be lost as a consequence of congestion in the network, causing a degradation in video quality at the receiver side. Predicting the packet loss probability is therefore an important step in the design of an efficient network-adaptive(More)