Abstract
We propose a framework for the automatic grouping and alignment of unedited multi-camera User-Generated Videos (UGVs) within a database. The proposed framework analyzes the sound in order to match and cluster UGVs that capture the same spatio-temporal event and estimate their relative time-shift to temporally align them. We design a descriptor derived from the pairwise matching of audio chroma features of UGVs. The descriptor facilitates the definition of a classification threshold for automatic query-by-example event identification. We evaluate the proposed identification and synchronization framework on a database of 263 multi-camera recordings of 48 real-world events and compare it with state-of-the-art methods. Experimental results show the effectiveness of the proposed approach in the presence of various audio degradations.
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