ABSTRACT
Cameras are one of the most utilized physical sensors that monitor our world. However, high bandwidth requirements and privacy concerns impede sharing the data with the public, who could benefit from being notified about ongoing situations. In contrast, smart cameras are currently designed for dedicated scenarios, i.e., users are limited by the predefined algorithms on board. In this work, we demonstrate a novel paradigm of tweeting cameras for event detection and recognition which can be customized by users for different purposes. Similar to humans, the camera is able to "tweet" via social networks, once it detects events of interest, instead of continuously streaming video data. By following the camera and replying to its tweets, humans can join the sensing loop and help the camera to improve its self-learning. We showcase our system using face and general event recognition scenarios, where the camera learns from humans what it has captured and tweets once the event status changes.
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