Evidence map›Paper›PMID 38931801›Full record

ArticleSensors (Basel, Switzerland)2024

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

Tianyi Zhang, Kohei Shimasaki, Idaku Ishii, Akio Namiki

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tianyi ZhangNamiki Laboratory, Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba-shi 263-8522, Chiba, Japan.ORCID 0009-0004-9261-6781
Kohei ShimasakiSmart Robotics Laboratory, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima 739-8527, Hiroshima, Japan.ORCID 0000-0003-4773-1575
Idaku IshiiSmart Robotics Laboratory, Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima 739-8527, Hiroshima, Japan.ORCID 0000-0002-7728-2363
Akio NamikiNamiki Laboratory, Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba-shi 263-8522, Chiba, Japan.ORCID 0000-0003-3181-0818

Funding

Japan Science and Technology Agency JP-MJSP2019
6 · The paper itself

Abstract

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.

Indexed as

autofocushigh-speed visionliquid lensobject tracking

Identifiers

PMID38931801
PMCPMC11207494

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.