Title

Complex Events Detection Using Data-Driven Concepts

Abstract

Automatic event detection in a large collection of unconstrained videos is a challenging and important task. The key issue is to describe long complex video with high level semantic descriptors, which should find the regularity of events in the same category while distinguish those from different categories. This paper proposes a novel unsupervised approach to discover data-driven concepts from multi-modality signals (audio, scene and motion) to describe high level semantics of videos. Our methods consists of three main components: we first learn the low-level features separately from three modalities. Secondly we discover the data-driven concepts based on the statistics of learned features mapped to a low dimensional space using deep belief nets (DBNs). Finally, a compact and robust sparse representation is learned to jointly model the concepts from all three modalities. Extensive experimental results on large in-the-wild dataset show that our proposed method significantly outperforms state-of-the-art methods. © 2012 Springer-Verlag.

Publication Date

10-30-2012

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

7574 LNCS

Issue

PART 3

Number of Pages

722-735

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-642-33712-3_52

Socpus ID

84867886443 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84867886443

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