In large-scale multimedia event detection, complex target events are extracted from a large set of\r\nconsumer-generated web videos taken in unconstrained environments. We devised a multimedia event detection\r\nmethod based on Gaussian mixture model (GMM) supervectors and support vector machines. A GMM supervector\r\nconsists of the parameters of a GMM for the distribution of low-level features extracted from a video clip. A GMM is\r\nregarded as an extension of the bag-of-words framework to a probabilistic framework, and thus, it can be expected to\r\nbe robust against the data insufficiency problem. We also propose a camera motion cancelled feature, which is a\r\nspatio-temporal feature robust against camera motions found in consumer-generated web videos. By combining\r\nthese methods with the existing features, we aim to construct a high-performance event detection system. The\r\neffectiveness of our method is evaluated using TRECVID MED task benchmark.
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