Energy-efficient secure vision processing applying object detection algorithms

摘要

Energy is optimized in a battery-powered camera system by co-locating a low-power vision processor with a camera. The vision processor executes algorithms to determine whether the image contains one or more objects of interest. Convolutional neural network is one example of an object detection algorithm. Energy is saved by making local decisions to turn off the camera for one or more subsequent frames, and by avoiding energy expenditure for compression and transmission. Security is optimized by transmitting only information about the images, as opposed to images themselves. Alternatively, security may be enhanced by completing a first portion of an object detection algorithm on a local processor, then transmitting interim data to a remote computer where a second portion of the algorithm is completed. It is challenging to obtain original image data from transmitted interim data.

主权项

1) A camera system is comprised of a camera, a vision processor co-located with the camera and executing an object recognition algorithm, and means for transmission of data to a remote computer, wherein:
said camera acquires an image; said vision processor executes an object recognition algorithm and outputs an indication on whether one or more objects of interest are included in the image; when indication is that no objects of interest are present in the image the camera and vision processor are placed in a mode to minimize energy consumption for a time equal to at least one frame period at the specified frame rate; when indication is that one or more objects of interest are present in the image, a video stream is initiated, video is compressed and transmitted to a remote computer.