This paper presents a novel dual-camera system that can simultaneously capture zoomed-in images using an ultrafast pan-tilt camera and a fixed wide-view camera using deep learning methods. An ultrafast pan-tilt camera can function as multiple virtual pan-tilt cameras by synchronizing a high-frame-rate zooming-view camera and an ultrafast pan-tilt mirror device that can switch over 500 different views in a second. A wide-view camera can obtain images in a fixed view in which multiple targets to be tracked are captured in low resolution and then recognized by processing the images using deep learning methods at a rate of dozens of frames per second. Based on the positions of all targets, recognized by the wide-view camera, the pan and tilt angles of multiple pan-tilt cameras are virtually controlled using an ultrafast pan-tilt camera through multithread viewpoint control to simultaneously capture the zoomed-in images of all targets. Our developed system can operate 10 virtual pan-tilt cameras (25 fps) with multithread viewpoint control and 4 ms time granularity in synchronization with convolutional neural-network-based recognition model operating at 25 fps, which is accelerated by a general-purpose computing on graphics processing units. The effectiveness of our system was demonstrated by the results of several experiments conducted on simultaneous zoom shooting of multiple running objects (persons and cars) in the range of approximately 80 m or higher in a natural outdoor scene, which was formerly too wide for a single fixed camera to capture clearly and simultaneously.