Big Davids – Real-Time Sensor Classification. Real-time classification of gorilla video segments in affective categories using crowd-sourced annotations
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In this sheet book we present a method to classify segments of gorilla videos in different affective categories. The classification method is trained by crowd sourcing annotation. The trained classification than uses video features (computed from the video segments) to classify a new video segment into different categories: fun, boring, scary, moving. As features we propose to use features largely based on optical flow. As classification method we propose to use k-NN for quick relearning possibilities. We validate our method with an experiment with multiple recordings of gorillas from different video cameras and an annotation crowd from within the TNO company.
TNO Identifier
482050
Publisher
TNO
Place of publication
Delft
Files
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