This study proposes a concept for multithread active vision sensing that can measure dynamically changing displacement and vibration at multiple points on civil engineering structures. In multithread active vision sensing, a high-speed camera can function virtually as multiple tracking cameras by accelerating its measurement, computation, and actuation with ultrafast viewpoint switching at millisecond level. This enables simultaneous measurement of small vibrations distributed across a wide range, which cannot be observed using a single camera system, because its pixel accuracy is generally incompatible with its wide angle of view. We developed a galvanomirror-based high-speed multithread active vision system that can switch 500 different views in a second; it functioned as 15 virtual cameras each operating at 33.3 fps to observe multiple scenes in completely different views. The experimental results for a 4-m-long truss-structure bridge model to which 15 markers were attached show that a single active vision system can observe the deformation of the bridge structure and estimate modal parameters, such as resonant frequencies and mode shapes, at a frequency on the order of dozens of hertz.

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.

This study proposes a visual tracking system with 500-fps image processing that can detect and track a fast-moving appearance-varying target in a 360-degree-field of view (FOV) from high-spatial-resolution images. The system consists of a high-speed camera, a pan-tilt Galvano-mirror system, and a mirror-drive high-speed pan control system with 360-degree-rotational capacity. Omnidirectional visual tracking of a remote object using complex visual features becomes possible due to the acquisition of high-spatial-resolution images of the target object via the system. We introduce an intelligent high frame rate (HFR) target-tracking algorithm that can simultaneously detect and track an appearance-varying object against a complex background for high-speed visual feedback control. Furthermore, the proposed surveillance system enables robust visual tracking under low-light conditions using a 3-D light detection and ranging (LiDAR) sensor system and a simultaneously controlled active spotlight projection system. The effectiveness of the proposed omnidirectional visual tracking system was demonstrated by conducting several omnidirectional detecting and tracking experiments on persons dozens of meters away using a 25-mm focal length lens under both daytime and night-time conditions.

This paper presents a novel concept of real-time catadioptric stereo tracking using a single ultrafast mirror-drive pan-tilt active vision system that can simultaneously switch between hundreds of different views in a second. By accelerating video-shooting, computation, and actuation at the millisecond-granularity level for time-division multithreaded processing in ultrafast gaze control, the active vision system can function virtually as two or more tracking cameras with different views. It enables a single active vision system to act as virtual left and right pan-tilt cameras that can simultaneously shoot a pair of stereo images for the same object to be observed at arbitrary viewpoints by switching the direction of the mirrors of the active vision system frame by frame. We developed a monocular galvano-mirror-based stereo tracking system that can switch between 500 different views in a second, and it functions as a catadioptric active stereo with left and right pan-tilt tracking cameras that can virtually capture 8-bit color $512\times 512$ images each operating at 250 fps to mechanically track a fast-moving object with a sufficient parallax for accurate 3D measurement. Several tracking experiments for moving objects in 3D space are described to demonstrate the performance of our monocular stereo tracking system.