In this paper, we present an automatic extraction of goal events in soccer videos by using audio track features alone without relying on expensive-to-compute video track features. The extracted goal events can be used for high-level indexing and selective browsing of soccer videos. The detection of soccer video highlights using audio contents comprises three steps: 1) extraction of audio features from a video sequence, 2) event candidate detection of highlight events based on the information provided by the feature extraction Methods and the Hidden Markov Model (HMM), 3) goal event selection to finally determine the video intervals to be included in the summary. For this purpose we compared the performance of the well known Mel-scale Frequency Cepstral Coefficients (MFCC) feature extraction method vs. MPEG-7 Audio Spectrum Projection feature (ASP) extraction method based on three different decomposition methods namely Principal Component Analysis( PCA), Independent Component Analysis (ICA) and Non-Negative Matrix Factorization (NMF). To evaluate our system we collected five soccer game videos from various sources. In total we have seven hours of soccer games consisting of eight gigabytes of data. One of five soccer games is used as the training data (e.g., announcers' excited speech, audience ambient speech noise, audience clapping, environmental sounds). Our goal event detection results are encouraging.

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Abstract-With the increasing amount of multimedia data, efficient tools for search and retrieval are needed. Since people are naturally one of the most interesting objects within these documents, a system for multimodal person search and retrieval has been developed. It combines the audiovisual analysis of persons with the query by example paradigm and relevance feedback to provide an efficient tool for searching multimedia data. For the relevance feedback, one and two class approaches are considered and compared to each other. Multimodal fusion techniques are used to exploit the complementary character of the audio and video information. The experimental results prove that multimodal person search and retrieval is feasible and more efficient than manual exploration.

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