Liciotti, Daniele and Duckett, Tom and Bellotto, Nicola and Frontoni, Emanuele and Zingaretti, Primo (2017) HMM-based activity recognition with a ceiling RGB-D camera. In: ICPRAM - 6th International Conference on Pattern Recognition Applications and Methods, 24 - 26 Feb 2017, Porto, Portugal.
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|Item Type:||Conference or Workshop contribution (Presentation)|
|Item Status:||Live Archive|
Automated recognition of Activities of Daily Living allows to identify possible health problems and apply corrective strategies in Ambient Assisted Living (AAL). Activities of Daily Living analysis can provide very useful information for elder care and long-term care services. This paper presents an automated RGB-D video analysis system that recognises human ADLs activities, related to classical daily actions. The main goal is to predict the probability of an analysed subject action. Thus, the abnormal behaviour can be detected. The activity detection and recognition is performed using an affordable RGB-D camera. Human activities, despite their unstructured nature, tend to have a natural hierarchical structure; for instance, generally making a coffee involves a three-step process of turning on the coffee machine, putting sugar in cup and opening the fridge for milk. Action sequence recognition is then handled using a discriminative Hidden Markov Model (HMM). RADiaL, a dataset with RGB-D images and 3D position of each person for training as well as evaluating the HMM, has been built and made publicly available.
|Keywords:||ADLs, Human Activity Recognition, HMMs|
|Subjects:||G Mathematical and Computer Sciences > G700 Artificial Intelligence|
G Mathematical and Computer Sciences > G740 Computer Vision
|Divisions:||College of Science > School of Computer Science|
|Deposited On:||15 Dec 2016 20:27|
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