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

Li, Qing; Hu, Shaopeng; Shimasaki, Kohei; Ishii, Idaku

doi:10.3390/s23084150

Cited by 6 publications

(3 citation statements)

References 76 publications

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“…Using it as a base, they further expanded the FoV by combining it with a high-speed rotating mirror [74]. Recently, Li et al enhanced this system to track multiple types of small objects simultaneously [75]. However, the fixed-focus lenses in these systems constrain their working depth range.…”

Section: Development Of High-speed Active Vision Systemsmentioning

confidence: 99%

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High-Magnification Object Tracking with Ultra-Fast View Adjustment and Continuous Autofocus Based on Dynamic-Range Focal Sweep

Zhang,

Shimasaki,

Ishii

et al. 2024

Sensors

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Active vision systems (AVSs) have been widely used to obtain high-resolution images of objects of interest. However, tracking small objects in high-magnification scenes is challenging due to shallow depth of field (DoF) and narrow field of view (FoV). To address this, we introduce a novel high-speed AVS with a continuous autofocus (C-AF) approach based on dynamic-range focal sweep and a high-frame-rate (HFR) frame-by-frame tracking pipeline. Our AVS leverages an ultra-fast pan-tilt mechanism based on a Galvano mirror, enabling high-frequency view direction adjustment. Specifically, the proposed C-AF approach uses a 500 fps high-speed camera and a focus-tunable liquid lens operating at a sine wave, providing a 50 Hz focal sweep around the object’s optimal focus. During each focal sweep, 10 images with varying focuses are captured, and the one with the highest focus value is selected, resulting in a stable output of well-focused images at 50 fps. Simultaneously, the object’s depth is measured using the depth-from-focus (DFF) technique, allowing dynamic adjustment of the focal sweep range. Importantly, because the remaining images are only slightly less focused, all 500 fps images can be utilized for object tracking. The proposed tracking pipeline combines deep-learning-based object detection, K-means color clustering, and HFR tracking based on color filtering, achieving 500 fps frame-by-frame tracking. Experimental results demonstrate the effectiveness of the proposed C-AF approach and the advanced capabilities of the high-speed AVS for magnified object tracking.

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Section: Development Of High-speed Active Vision Systemsmentioning

confidence: 99%

“…Recently, Li et al. enhanced this system to track multiple types of small objects simultaneously [ 75 ]. However, the fixed-focus lenses in these systems constrain their working depth range.…”

Section: Related Workmentioning

confidence: 99%

High-Magnification Object Tracking with Ultra-Fast View Adjustment and Continuous Autofocus Based on Dynamic-Range Focal Sweep

Zhang,

Shimasaki,

Ishii

et al. 2024

Sensors

Self Cite

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show abstract

“…Garbage classification [ 26 ], cloud classification [ 27 ], clothing classification [ 28 ], fruit quality classification [ 29 ], and housing classification [ 30 ] are the application examples. In addition, a CNN-based hybrid tracking algorithm was developed to robustly track multiple high-speed moving objects simultaneously in [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

CNN-Based QR Code Reading of Package for Unmanned Aerial Vehicle

Yang

Jan

Chen

et al. 2023

Sensors

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This paper plans to establish a warehouse management system based on an unmanned aerial vehicle (UAV) to scan the QR codes printed on packages. This UAV consists of a positive cross quadcopter drone and a variety of sensors and components, such as flight controllers, single-board computers, optical flow sensors, ultrasonic sensors and cameras, etc. The UAV stabilizes itself by proportional-integral-derivative (PID) control and takes pictures of the package as it reaches ahead of the shelf. Through convolutional neural networks (CNNs), the placement angle of the package can be accurately identified. Some optimization functions are applied to compare system performance. When the angle is 90°, that is, the package is placed normally and correctly, the QR code will be read directly. Otherwise, image processing techniques that include Sobel edge computing, minimum circumscribed rectangle, perspective transformation, and image enhancement is required to assist in reading the QR code. The experimental results showed that the proposed algorithm provided good performance of a recognition rate of 94% for the stochastic gradient descent (SGD) and 95% for Adadelta optimization functions. After that, successful QR code reading was presented.

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Vision-based displacement measurement method of large-scale bridges using tilt shift camera and fast spatio-temporal context learning

Guo,

Li,

2025

Mechanical Systems and Signal Processing

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

Cited by 6 publications

References 76 publications

High-Magnification Object Tracking with Ultra-Fast View Adjustment and Continuous Autofocus Based on Dynamic-Range Focal Sweep

High-Magnification Object Tracking with Ultra-Fast View Adjustment and Continuous Autofocus Based on Dynamic-Range Focal Sweep

CNN-Based QR Code Reading of Package for Unmanned Aerial Vehicle

Vision-based displacement measurement method of large-scale bridges using tilt shift camera and fast spatio-temporal context learning

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