| Summary: | 碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 101 === We present a simple but effective online recognition system for detecting
interleaved activities of daily life (ADLs) in first-person-view videos. The
two major difficulties in detecting ADLs are interleaving and variability in
duration. We use temporal pyramid in our system to attack these difficulties,
and this means we can use relatively simple models instead of time dependent
probability ones such as Hidden semi-Markov model or nested models.
The proposed solution includes the combination of conditional random fields
(CRF) and an online inference algorithm, which explicitly considers multiple
interleaved sequences by inferencing multi-stage activities on temporal
pyramid. Although our system only uses linear chain-structured CRF model,
which can be easily learned without a large amount of training data, it still
recognizes complicated activity sequences. The system is evaluated on a data
set provided by the work from state-of-the-art, and the result is comparable
to their method. We also provide some experiment result using a customized
dataset.
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