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We present a closed-loop unsupervised clustering method for motion
vectors extracted from highly dynamic video scenes. Motion vectors are assigned
to non-convex homogeneous clusters characterizing direction, size and shape of
regions with multiple independent activities. The proposed method is based on
Support Vector Clustering (SVC). Cluster labels are propagated over time via
incremental learning. The proposed method uses a kernel function that maps the
input motion vectors into a high-dimensional space to produce non-convex
clusters. We improve the mapping effectiveness by quantifying feature
similarities via a blend of position and orientation affinities. We use the Quasiconformal Kernel Transformation to boost the
discrimination of outliers. The temporal propagation of the clusters'
identities is achieved via incremental learning based on the concept of feature
obsolescence to deal with appearing and disappearing features. Moreover, we
design an on-line clustering performance prediction algorithm used as a
feedback (closed-loop) that refines the cluster model at each frame in an
unsupervised manner. We evaluate the proposed method on synthetic datasets and
real-world crowded videos, and show that our solution outperforms
state-of-the-art approaches.
I.A. Lawal, F. Poiesi, D. Anguita, A. Cavallaro, "Support Vector Motion Clustering,"
IEEE Transactions on Circuits and Systems for Video Technology, (to appear) pdf
This work was supported in part by the Erasmus Mundus Joint
Doctorate in Interactive and Cognitive Environments (which is funded by the
EACEA Agency of the European Commission under EMJD ICE FPA n 2010-0012) and by
the Artemis JU and the UK Technology Strategy Board through COPCAMS Project
under Grant 332913.
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