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Such systems could be used for activity recognition applications in a variety of domains.
Reducing the number of resources used for activity recognition is a key to enhance the sensor network operating time [46].
In the proposed system, each moving human can potentially be observed from multiple cameras, generating multiple observation sequences that can be used for activity recognition (see Figure 4).
The subsequences between the change points are processed to find their Activity Areas (AA) and Activity History Areas (AHA) that are used for activity recognition.
The Activity Areas and their temporal weighted versions, the Activity History Areas, for the extracted subsequences are used for activity recognition.
For the learning models, the RF model was used for activity recognition, while the RF, LMT, and MP models were used for identity recognition.
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Different methods have also used this dataset for activity recognition.
Both the shape of the Activity Area and motion information from the Activity History Area are used for accurate activity recognition, as detailed in the sections that follow.
Besides natural language processing [19-21] [19-21]ave previousLTMseen used for haven activity recognition [22], image annotation and segmentation [23], and image-based object recognition [24].
Such systems could be used for unobtrusive activity recognition applications in a variety of domains.
They use available sensors for activity recognition.
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