A Flexible Calibration Algorithm for High-speed Bionic Vision System based on Galvanometer

Li, Qing; Chen, Mengjuan; Gu, Qingyi; Ishii, Idaku

doi:10.1109/iros47612.2022.9981947

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Due to the limited movement speed of the gimbal and the size of the telephoto lens, it is difficult for gimbal-based PTZ cameras to move at high speeds and observe multiple objects [13]. Compared to traditional camera systems operating at 30 or 60 fps, high-speed vision systems can work at 1000 fps or more [14]. The high-speed vision system acquires and processes image information with extremely low latency and interacts with the environment through visual feedback control [15].…”

Section: Introductionmentioning

confidence: 99%

An Active Multi-Object Ultrafast Tracking System with CNN-Based Hybrid Object Detection

Shimasaki

et al. 2023

Sensors

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This study proposes a visual tracking system that can detect and track multiple fast-moving appearance-varying targets simultaneously with 500 fps image processing. The system comprises a high-speed camera and a pan-tilt galvanometer system, which can rapidly generate large-scale high-definition images of the wide monitored area. We developed a CNN-based hybrid tracking algorithm that can robustly track multiple high-speed moving objects simultaneously. Experimental results demonstrate that our system can track up to three moving objects with velocities lower than 30 m per second simultaneously within an 8-m range. The effectiveness of our system was demonstrated through several experiments conducted on simultaneous zoom shooting of multiple moving objects (persons and bottles) in a natural outdoor scene. Moreover, our system demonstrates high robustness to target loss and crossing situations.

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Section: Introductionmentioning

confidence: 99%

An Active Multi-Object Ultrafast Tracking System with CNN-Based Hybrid Object Detection

Shimasaki

et al. 2023

Sensors

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“…(3) Target localization is often inaccurate in wide-area FOV due to the small size of object in an image, making accurate localization difficult. Existing image capture methods, which often use wideangle [9] or fish-eye lenses [10], exacerbate the difficulty of target localization by introducing image distortion and quality degradation [11]. To address the intricate challenges in exists wide-area visual systems, numerous methods have been developed.…”

Section: Introductionmentioning

confidence: 99%

Region Probability Map-Guided Fast Wide-Area Multiobject Detection

Long

Chen

et al. 2023

IEEE Trans. Instrum. Meas.

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In recent years, wide-area visual surveillance systems have been widely applied in various industrial and transportation scenarios. These systems, however, face significant challenges when implementing multi-object detection due to conflicts arising from the need for high-resolution imaging, efficient object searching, and accurate localization. To address these challenges, this paper presents a hybrid system that incorporates a wide-angle camera, a high-speed search camera, and a galvano-mirror. In this system, the wide-angle camera offers panoramic images as prior information, which helps the search camera capture detailed images of the targeted objects. This integrated approach enhances the overall efficiency and effectiveness of wide-area visual detection systems. Specifically, in this study, we introduce a wide-angle camera-based method to generate a panoramic probability map (PPM) for estimating high-probability regions of target object presence. Then, we propose a probability searching module that uses the PPMgenerated prior information to dynamically adjust the sampling range and refine target coordinates based on uncertainty variance computed by the object detector. Finally, the integration of PPM and the probability searching module yields an efficient hybrid vision system capable of achieving 120 fps multi-object search and detection. Extensive experiments are conducted to verify the system's effectiveness and robustness.

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“…Spatial similarity only considers the same geometric shape and ignores the temporal dimension, which is not suitable for real-time stereo correspondence systems. The update of motion information is delayed due to the limited speed of traditional visual image input (30 or 60 fps) [18], making trajectory synchronization of high-speed moving objects difficult. However, high-speed vision sensors operate at hundreds or even higher frequencies, enabling them to observe moving objects and capture phase differences with extremely low latency [19].…”

Section: Introductionmentioning

confidence: 99%

HFR-Video-Based Stereo Correspondence Using High Synchronous Short-Term Velocities

Shimasaki

et al. 2023

Sensors

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This study focuses on solving the correspondence problem of multiple moving objects with similar appearances in stereoscopic videos. Specifically, we address the multi-camera correspondence problem by taking into account the pixel-level and feature-level stereo correspondences, and object-level cross-camera multiple object correspondence. Most correspondence algorithms rely on texture and color information of the stereo images, making it challenging to distinguish between similar-looking objects, such as ballet dancers and corporate employees wearing similar dresses, or farm animals such as chickens, ducks, and cows. However, by leveraging the low latency and high synchronization of high-speed cameras, we can perceive the phase and frequency differences between the movements of similar-looking objects. In this study, we propose using short-term velocities (STVs) of objects as motion features to determine the correspondence of multiple objects by calculating the similarity of STVs. To validate our approach, we conducted stereo correspondence experiments using markers attached to a metronome and natural hand movements to simulate simple and complex motion scenes. The experimental results demonstrate that our method achieved good performance in stereo correspondence.

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A Flexible Calibration Algorithm for High-speed Bionic Vision System based on Galvanometer

Cited by 7 publications

References 9 publications

An Active Multi-Object Ultrafast Tracking System with CNN-Based Hybrid Object Detection

An Active Multi-Object Ultrafast Tracking System with CNN-Based Hybrid Object Detection

Region Probability Map-Guided Fast Wide-Area Multiobject Detection

HFR-Video-Based Stereo Correspondence Using High Synchronous Short-Term Velocities

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