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- W4376458047 abstract "Online activity detection means that the model gives the type of action that is happening in real time which is important for management and security of data center in energy stations. Online activity detection is more practical than offline activity detection for such video surveillance cases. However, online activity detection is more difficult in that only part video stream of an activity is available. So we construct invisible future features and connect them with historical features for the classification of current actions. We propose a novel two-stage Predict-Classify TRansformers (PCTR) for online activity detection with two transformers, where one is for predicting features of near future, and the other is for classifying activity by incorporating observed information and the predicted future information. More specifically, the predicting transformer has an encoder and a decoder, where the encoder models the temporal context of observed information, and the decoder predicts the near future features. Then the encoder of the classifying transformer recognizes ongoing activity by integrating observed features and the predicted features. We conduct extensive experiments on THUMOS’14 datasets to validate the proposed PCTR and get excellent performances." @default.
- W4376458047 created "2023-05-14" @default.
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- W4376458047 date "2022-11-11" @default.
- W4376458047 modified "2023-10-14" @default.
- W4376458047 title "Online Activity Detection with Predicting and Classifying Transformers" @default.
- W4376458047 doi "https://doi.org/10.1109/ei256261.2022.10117161" @default.
- W4376458047 hasPublicationYear "2022" @default.
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