Abstract
We present a scene understanding strategy for video sequences based on clustering object trajectories. In this chapter, we discuss a set of relevant feature spaces for trajectory representation and we critically analyze their relative merits. Next, we examine various trajectory clustering methods that can be employed to learn activity models, based on their classification into hierarchical and partitional algorithms. In particular, we focus on parametric and non-parametric partitional algorithms and discuss the limitations of existing approaches. To overcome the limitations of state-of-the-art approaches we present a soft partitional algorithm based on non-parametric Mean-shift clustering. The proposed algorithm is validated on real datasets and compared with state-of-the-art approaches, based on objective evaluation metrics.
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Anjum, N., Cavallaro, A. (2010). Trajectory Clustering for Scene Context Learning and Outlier Detection. In: Schonfeld, D., Shan, C., Tao, D., Wang, L. (eds) Video Search and Mining. Studies in Computational Intelligence, vol 287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12900-1_2
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DOI: https://doi.org/10.1007/978-3-642-12900-1_2
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