Activity Recognition in First-Person Camera View Based onTemporal Pyramid

• Main Authors: Hsuan-Ming Liu, 劉軒銘
• Other Authors: Ming Ouhyoung
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/92962830683022916719

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.