In this paper, we develop a multiple-object zooming system which can capture clear images of different objects at an ultrafast speed. The system consists of a panoramic HFR stereo camera and a galvanometer-based reflective pan-tilt-zoom (PTZ) camera. In order to alleviate the impact of brightness, noise, and viewing angle in the image, we use the high speed motion information of the object for stereo correspondence. According to the spatial positions of all objects obtained from HFR stereo correspondence, we can obtain the control voltage of the pan and tilt mirror of the galvanometer-based reflective PTZ camera through the mapping relationship. Then, PTZ camera captures clear images of multiple objects in a time-division multiplexed manner at an extremely fast speed. Experimental results show that we can distinguish multiple fast-moving people indoors in the HFR stereo camera and capture their high-definition facial images simultaneously.

This paper presents an innovative continuous autofocus (C-AF) approach based on high-speed vision. It consistently provides focused images with stable and sufficiently high frame rates, aiming to improve the ability to track small, fast-moving objects in a highly magnified scene. To achieve this, we propose the concept of a dynamic-range focal sweep enabled by a high-speed camera and a focus-tunable liquid lens with high adjustment capability. The focal sweep consistently covers a small range around the object’s focus position, guided by previous depth results obtained through the depth-from-focus (DFF) technique. We conducted verification experiments to thoroughly analyze the capability of the proposed C-AF approach. By integrating a 2-axis Galvano mirror, we built a high-speed C-AF active vision system with rapid focus and pan-tilt adjustments. The comprehensive experiment results highlight the advanced capabilities of our seamless C-AF in tracking magnified objects.

Analysis of the behavior between the wheel and the road surface is important for understanding a vehicle's safe traveling performance. In contrast, sensors attached to the wheels have many limitations, and it is difficult to capture slipping phenomena such as spinning and sliding with rotation sensors alone. In addition, it is difficult for a fixed-point camera to capture phenomena between the wheel and the road surface that cannot be predicted when and where they will occur because the angle of view is limited. Here, high-speed zoom tracking was performed remotely using ultra-high-speed active vision. Image analysis of the rotating wheel, which is always magnified at the center of the high frame rate image, enabled non-contact measurement of wheel behavior while the vehicle moved. Experiments were conducted using actual N-gauge model vehicles and Segways to clarify the slipping phenomena of running vehicles and analysis of wheel deformation behavior. By analyzing tracking zoomed images taken at 500 fps, we quantitatively verified the measurement of slip during sudden stop and sudden acceleration and wheel deformation behavior during running over hump.

This study proposes a dual-camera system for indoor high magnification surveillance which is capable of achieving always-in-focus and non-delay gaze control based on high-speed vision. The users are enabled to move the mouse freely on the wide-view screen while observing its in-focal zoom-in monitoring video in real-time. The proposed system consists of a wide-angle camera for wide-view and a Galvano mirror-enabled ultra-fast pan-tilt-zoom (PTZ) camera for zoom-in view. To achieve always-in-focus, a high-speed focus scanning system is proposed that is comprised of a high-speed camera, a parfocal zoom lens, and a gear mechanism. Through continuously reciprocating rotational motion of the focusing ring driven by the servo motor, the high-speed camera captures sets of images with varying focal distances. Moreover, we proposed a most-in-focus (MIF) frame extraction algorithm to select the sharpest images as output. The experimental results are obtained to confirm the effectiveness of our system.

We developed a dual-camera-based multi-face zoom-tracking system that captures zoomed targets simultaneously by combining a mirror-drive pan-tilt camera with ultrafast gaze control for zoomed views and an RGB-D camera for 3D wide view. In our system, the RGB-D camera detects multiple persons in the wide view and outputs the 3-D positions of their keypoints with CNN-based pose estimation. The mirror-drive pan-tilt camera simultaneously switches its viewpoints to the directions of the nose keypoints to zoom on their face images to perform person identification. We demonstrate our system performance using experimental results of a 3-D people-flow analysis with person identification, where the mirror-drive pantilt camera with 120-viewpoints/s-switching functions effectively as five virtual 24-fps pan-tilt cameras to track and zoom on the faces of five walking persons in an indoor scene.