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Generating Unsupervised Models for Online Long-Term Daily Living Activity Recognition

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Academic year: 2021

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Figure

Figure 1. (a) a sample of scene regions clustered using trajectory information, (b) a sample of sequence of Primitive Actions and Discovered Activities in three levels of granularity.
Figure 2. Creating activity models as a tree of nodes using discovered activities in training set
Figure 3. A sample of activities in datasets: (a) answering phone, (b) preparing drink, (c) establishing account balance, (d) prepare drug box, (e) watering plant, (f) reading, (g) turning on radio, (h) using bus map.
Table 1. The activity recognition results for GAADRD dataset. Bold values represent the best sensitivity and precision results for each class.

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