Omnidirectional Panoramic Video System With Frame-by-Frame Ultrafast Viewpoint Control

Hu, Shaopeng; Dong, Hongyu; Shimasaki, Kohei; Jiang, Mingjun; Senoo, Taku; Ishii, Idaku

doi:10.1109/lra.2022.3150484

Cited by 10 publications

(3 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results are listed in Table I. In this experiment, we compare the PPM method against three region probabilitybased algorithms, which include the Omni-Pano [15], MPF searching [14], and RPM-guided detection method [25]. During the experiment, we changed the proportion of high-…”

Section: Recall Comparison Of Different Searching Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Region Probability Map-Guided Fast Wide-Area Multiobject Detection

Long

Chen

et al. 2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

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.

show abstract

Section: Recall Comparison Of Different Searching Methodsmentioning

confidence: 99%

“…Omni-Pano [15] 0.51 0.62 0.66 0.72 0.73 MPF [14] 0.63 0.72 0.76 0.82 0.84 RPM-Guided [25] 0.89 0.87 0.84 0.83 0.85 PPM (Ours) 0.93 0.90 0.87 0.86 0.85 probability regions from 27% to 63%. As can be seen from Table I, owing to the PPM prior information generation module, our method gets the best recall rates at different proportions.…”

Section: Methodsmentioning

confidence: 99%

Region Probability Map-Guided Fast Wide-Area Multiobject Detection

Long

Chen

et al. 2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

show abstract

“…Their team also designed a multiple body key-part tracking system with only one camera [73]. 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].…”

Section: Development Of High-speed Active Vision Systemsmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Rotating mirror servo system control based on modified sliding mode-active disturbance rejection controller

Zhuang

et al. 2022

Systems Science & Control Engineering

View full text Add to dashboard Cite

Omnidirectional Panoramic Video System With Frame-by-Frame Ultrafast Viewpoint Control

Cited by 10 publications

References 56 publications

Region Probability Map-Guided Fast Wide-Area Multiobject Detection

Region Probability Map-Guided Fast Wide-Area Multiobject Detection

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

Rotating mirror servo system control based on modified sliding mode-active disturbance rejection controller

Contact Info

Product

Resources

About