A Hierarchical Self-Organizing Approach for Learning The Patterns of Motion Trajectories  

Oleh:

Hu, Weiming ; Xie, Dan ; Tan, Tieniu

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 15 no. 1 (Jan. 2004), page 135-144.
Topik: motions; hierarchical; self - organizing; approach; learning prediction; patterns; motion trajectories

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.10 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The understanding and description of object behaviours is a hot topic in computer vision. Trajectory analysis is one of the basic problems in behavior understanding, and the learning of trajectory patterns that can be used to detect anomalies and predict object trajectories is an interesting and important problem in trajectory analysis. In this paper, we present a hierarchical self - organizing neural network model and its application to the learning of trajectory distribution patterns for event recognition. The distribution patterns of trajectories are learnt using a hierarchical self - organizing neural network. Using the learned patterns, we consider anomaly detection as well as object behaviour prediction. Compared with the existing neural network structures that are used to learn patterns of trajectories, our network structure has smaller scale and faster learning speed, and is thus more effective. Experimental results using two different sets of data demonstrate the accuracy and speed of our hierarchical self - organizing neural network in learning the distribution patterns of object trajectories.

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