Detecting and Tracking Animals in Underwater Video
نویسندگان
چکیده
We demonstrate an attentional selection and tracking system for processing video streams from remotely operated underwater vehicles (ROVs). The system identifies and tracks potentially interesting visual events spanning multiple frames based on low-level properties of salient objects, which are associated with those events. If video frames contain interesting frames, they are labeled “interesting”, otherwise they are labeled “boring”. By marking the interesting events and omitting boring frames in the output stream, we augment the productivity of human video annotators, or, alternatively, provide input for a subsequent object classification algorithm.
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