@inproceedings{PAIR17_GranadaAndOthers,
  author =    {Roger Granada and Ramon Fraga Pereira and Juarez Monteiro and Rodrigo Barros and Duncan Ruiz and Felipe Meneguzzi},
  title =     {Hybrid Activity and Plan Recognition for Video Streams},
  booktitle = {The AAAI 2017 Workshop on Plan, Activity, and Intent Recognition (PAIR@AAAI)},
  year =      {2017},
  abstract = {Computer-based human activity recognition of daily living has recently attracted much interest due to its applicability to ambient assisted living. Such applications require the automatic recognition of high-level activities composed of multiple actions performed by human beings in an environment. In this work, we address the problem of activity recognition in an indoor environment, focusing on a kitchen scenario. Unlike existing approaches that identify single actions from video sequences, we also identify the goal towards which the subject of the video is pursuing. Our hybrid approach combines a deep learning architecture to analyze raw video data and identify individual actions which are then processed by a goal recognition algorithm that uses a plan library describing possible overarching activities to identify the ultimate goal of the subject in the video. Experiments show that our approach achieves the state-of-the-art for identifying cooking activities in a kitchen scenario.},
  url = {files/pair17-hybrid-recognizer.pdf}
}

{"_id":"XANkR6rrzyxkMXay4","bibbaseid":"granada-pereira-monteiro-barros-ruiz-meneguzzi-hybridactivityandplanrecognitionforvideostreams-2017","downloads":0,"creationDate":"2017-06-22T20:12:30.028Z","title":"Hybrid Activity and Plan Recognition for Video Streams","author_short":["Granada, R.","Pereira, R. F.","Monteiro, J.","Barros, R.","Ruiz, D.","Meneguzzi, F."],"year":2017,"bibtype":"inproceedings","biburl":"https://ramonpereira.github.io/publications/ramon-publications.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Roger"],"propositions":[],"lastnames":["Granada"],"suffixes":[]},{"firstnames":["Ramon","Fraga"],"propositions":[],"lastnames":["Pereira"],"suffixes":[]},{"firstnames":["Juarez"],"propositions":[],"lastnames":["Monteiro"],"suffixes":[]},{"firstnames":["Rodrigo"],"propositions":[],"lastnames":["Barros"],"suffixes":[]},{"firstnames":["Duncan"],"propositions":[],"lastnames":["Ruiz"],"suffixes":[]},{"firstnames":["Felipe"],"propositions":[],"lastnames":["Meneguzzi"],"suffixes":[]}],"title":"Hybrid Activity and Plan Recognition for Video Streams","booktitle":"The AAAI 2017 Workshop on Plan, Activity, and Intent Recognition (PAIR@AAAI)","year":"2017","abstract":"Computer-based human activity recognition of daily living has recently attracted much interest due to its applicability to ambient assisted living. Such applications require the automatic recognition of high-level activities composed of multiple actions performed by human beings in an environment. In this work, we address the problem of activity recognition in an indoor environment, focusing on a kitchen scenario. Unlike existing approaches that identify single actions from video sequences, we also identify the goal towards which the subject of the video is pursuing. Our hybrid approach combines a deep learning architecture to analyze raw video data and identify individual actions which are then processed by a goal recognition algorithm that uses a plan library describing possible overarching activities to identify the ultimate goal of the subject in the video. Experiments show that our approach achieves the state-of-the-art for identifying cooking activities in a kitchen scenario.","url":"files/pair17-hybrid-recognizer.pdf","bibtex":"@inproceedings{PAIR17_GranadaAndOthers,\n author = {Roger Granada and Ramon Fraga Pereira and Juarez Monteiro and Rodrigo Barros and Duncan Ruiz and Felipe Meneguzzi},\n title = {Hybrid Activity and Plan Recognition for Video Streams},\n booktitle = {The AAAI 2017 Workshop on Plan, Activity, and Intent Recognition (PAIR@AAAI)},\n year = {2017},\n abstract = {Computer-based human activity recognition of daily living has recently attracted much interest due to its applicability to ambient assisted living. Such applications require the automatic recognition of high-level activities composed of multiple actions performed by human beings in an environment. In this work, we address the problem of activity recognition in an indoor environment, focusing on a kitchen scenario. Unlike existing approaches that identify single actions from video sequences, we also identify the goal towards which the subject of the video is pursuing. Our hybrid approach combines a deep learning architecture to analyze raw video data and identify individual actions which are then processed by a goal recognition algorithm that uses a plan library describing possible overarching activities to identify the ultimate goal of the subject in the video. Experiments show that our approach achieves the state-of-the-art for identifying cooking activities in a kitchen scenario.},\n url = {files/pair17-hybrid-recognizer.pdf}\n}\n\n","author_short":["Granada, R.","Pereira, R. F.","Monteiro, J.","Barros, R.","Ruiz, D.","Meneguzzi, F."],"key":"PAIR17_GranadaAndOthers","id":"PAIR17_GranadaAndOthers","bibbaseid":"granada-pereira-monteiro-barros-ruiz-meneguzzi-hybridactivityandplanrecognitionforvideostreams-2017","role":"author","urls":{"Paper":"https://ramonpereira.github.io/publications/files/pair17-hybrid-recognizer.pdf"},"downloads":0,"html":""},"search_terms":["hybrid","activity","plan","recognition","video","streams","granada","pereira","monteiro","barros","ruiz","meneguzzi"],"keywords":[],"authorIDs":["593feebad788d2f259000001","594c25fd5b1e16be22000029","5e00b25c05b03cf3010000e6","5e1b376a749a45df0100004d","5e21c28f96aea7de01000164","9RRkqcZEYmwhehhxm","HPeJ46EkB3ZQgeiBQ","SY5SpvLW7BzbKsDrD","fszXNY7Jnyp4scNWf","gBzgeijXeNjPQXmdJ","rgBrw2Aru965AaeZA"],"dataSources":["vxfpC82j9rZsQ6KXP"]}