Multi-Thread AI Cameras Using High-Speed Active Vision System
Mingjun Jiang*1, Zihan Zhang*2, Kohei Shimasaki*3, Shaopeng Hu*4, and Idaku Ishii*4
*1Innovative Research Excellence, Honda R&D Co., Ltd.
Midtown Tower 38F, 9-7-1 Akasaka, Minato-ku, Tokyo 107-6238, Japan
*2DENSO TEN Limited
1-2-28 Goshodori, Hyogo-ku, Kobe 652-8510, Japan
*3Digital Monozukuri (Manufacturing) Education Research Center, Hiroshima University
3-10-32 Kagamiyama, Higashi-hiroshima, Hiroshima 739-0046, Japan
*4Graduate School of Advanced Science and Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan
In this study, we propose a multi-thread artificial intelligence (AI) camera system that can simultaneously recognize remote objects in desired multiple areas of interest (AOIs), which are distributed in a wide field of view (FOV) by using single image sensor. The proposed multi-thread AI camera consists of an ultrafast active vision system and a convolutional neural network (CNN)-based ultrafast object recognition system. The ultrafast active vision system can function as multiple virtual cameras with high spatial resolution by synchronizing exposure of a high-speed camera and movement of an ultrafast two-axis mirror device at hundreds of hertz, and the CNN-based ultrafast object recognition system simultaneously recognizes the acquired high-frame-rate images in real time. The desired AOIs for monitoring can be automatically determined after rapidly scanning pre-placed visual anchors in the wide FOV at hundreds of fps with object recognition. The effectiveness of the proposed multi-thread AI camera system was demonstrated by conducting several wide area monitoring experiments on quick response (QR) codes and persons in nature spacious scene such as meeting room, which was formerly too wide for a single still camera with wide angle lens to simultaneously acquire clear images.
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