Vehicle Detection and Disparity Estimation Using Blended Stereo Images

Zhou, Changxin; Liu, Yazhou; Lasang, Pongsak; Sun, Quansen

doi:10.1109/tiv.2020.3049008

Cited by 19 publications

(3 citation statements)

References 38 publications

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“…On the other hand, image-based depth regression methods make a breakthrough in depth estimation performance by virtue of CNN-based abstract feature extraction. No longer do such methods use feature matching between images, so as to overcome the mismatching effect of textureless and texturerepeated areas [77], [78], [79]. However, such methods tend to learn prior knowledge of depth estimation from training data and produce error depth prediction for the real-world scenarios that are obviously different from training scenarios.…”

Section: Indoor Depth Estimationmentioning

confidence: 99%

VirtualClassroom: A Lecturer-Centered Consumer-Grade Immersive Teaching System in Cyber–Physical–Social Space

Shen

Huang

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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Lecturers, as the guidance of the classroom, play a significant role in the teaching process. However, the lecturers' sense of space immersion has been ignored in current virtual teaching systems. In this article, we explore the cyber-physicalsocial intelligence for Edu-Metaverse in cyber-physical-social space and specially design a lecturer-centered immersive teaching system, taking the social and lecturers' factors into consideration. We call this system VirtualClassroom (V-Classroom). Specifically, we first introduce the cyber-physical-social system (CPSS) paradigm of V-Classroom so that the workflow is standardized and significantly simplified, and the systems can be constructed with off-the-shelf hardware. The key component of V-Classroom is a cyber-world representation of a physical-world classroom instrumented with sparse consumer-grade RGBD cameras for capturing the 3-D geometry and texture of the classrooms. We provide each V-Classroom lecturer with a physical device for sending 6DoF view-change messages and showing view-dependent content of the remote classroom. Following the above paradigm, we develop the V-Classroom algorithms, including V-Classroom depth algorithm (V-DA) and V-Classroom view algorithm (V-VA), to achieve the real-time rendering of remote classrooms. V-DA is dedicated to recovering accurate depth information of the classrooms while V-VA is devoted to real-time novel view synthesis. Finally, we illustrate our implemented CPSS-driven V-Classroom prototype, based on real-world classroom scenarios we collected, and discuss the main challenges and future direction.

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Section: Indoor Depth Estimationmentioning

confidence: 99%

VirtualClassroom: A Lecturer-Centered Consumer-Grade Immersive Teaching System in Cyber–Physical–Social Space

Shen

Huang

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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“…The number of equivalent axles loads of the section within the design life can be calculated from the traffic flow, which can be used to calculate the fatigue cracking life of the road stabilization layer, the permanent deformation of the material, the low temperature cracking index of the road layer, the thickness of the antifreeze layer and the deflection value of the top surface of the subgrade, etc. Traffic volume forecasting can be achieved through vehicle type detection and density detection [63], [64], [65], [66]. Such as historical traffic volume, annual growth rate of traffic volume, changes in road network, regional economic development, and policies are used for traffic volume forecasting.…”

Section: A M-rm System Based On Cpssmentioning

confidence: 99%

Cognitive-Based Crack Detection for Road Maintenance: An Integrated System in Cyber-Physical-Social Systems

Fan

Cao

Zeng

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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Effective road maintenance can not only achieve a balance between limited resources and long-term high-efficiency performance of road but also reduce the loss of life and property caused by road damage to vehicles and pedestrians. Due to the lack of a multidimensional dynamic monitoring system and enough extremely special data, the existing road maintenance system cannot accurately assess the road surface condition and provide timely early warning of sudden road damage. In this article, the M-RM system is proposed, that is, a metaverse-enabled road maintenance system based on cyber-physical-social systems (CPSSs), which fully utilizes the social and artificial system information of CPSS, as well as the simulation, monitoring, diagnosis and prediction functions of road systems in the virtual world of the metaverse. Then, in the road damage detection of system model in the virtual world, for the virtual data of the core assets of the metaverse, we propose an adaptive and information-preserving data augmentation (AIDA) algorithm-based nonclassical receptive field suppression and enhancement, an algorithm developed from human visual cognition. This algorithm enables the generation of a large amount of scarce fidelity data and avoids the introduced noise from impairing the performance of nonaugmented data. Finally, a crack detection algorithm named pay attention twice (PAT) is proposed, which uses the generated virtual data for training, and achieves secondary attention to high-frequency targets by fusing frequency-division convolution and mixed-domain attention mechanism. The detection performance of small targets in uncertain environments is enhanced. The metaverse system built in the current research can not only be used for road maintenance but also empower the traffic metaverse by using the traffic flow prediction module embedded in the algorithm.

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“…According to epipolar geometry, disparity [ 27 ] refers to the displacement of the same pixel in neighboring views. Since depth and disparity have an inverse proportional relationship, calculating disparities allows us to determine the scene’s depth.…”

Section: Related Workmentioning

confidence: 99%

A New Parallel Intelligence Based Light Field Dataset for Depth Refinement and Scene Flow Estimation

Shen

Liu

Tian

et al. 2022

Sensors

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Computer vision tasks, such as motion estimation, depth estimation, object detection, etc., are better suited to light field images with more structural information than traditional 2D monocular images. However, since costly data acquisition instruments are difficult to calibrate, it is always hard to obtain real-world scene light field images. The majority of the datasets for static light field images now available are modest in size and cannot be used in methods such as transformer to fully leverage local and global correlations. Additionally, studies on dynamic situations, such as object tracking and motion estimates based on 4D light field images, have been rare, and we anticipate a superior performance. In this paper, we firstly propose a new static light field dataset that contains up to 50 scenes and takes 8 to 10 perspectives for each scene, with the ground truth including disparities, depths, surface normals, segmentations, and object poses. This dataset is larger scaled compared to current mainstream datasets for depth estimation refinement, and we focus on indoor and some outdoor scenarios. Second, to generate additional optical flow ground truth that indicates 3D motion of objects in addition to the ground truth obtained in static scenes in order to calculate more precise pixel level motion estimation, we released a light field scene flow dataset with dense 3D motion ground truth of pixels, and each scene has 150 frames. Thirdly, by utilizing the DistgDisp and DistgASR, which decouple the angular and spatial domain of the light field, we perform disparity estimation and angular super-resolution to evaluate the performance of our light field dataset. The performance and potential of our dataset in disparity estimation and angular super-resolution have been demonstrated by experimental results.

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Vehicle Detection and Disparity Estimation Using Blended Stereo Images

Cited by 19 publications

References 38 publications

VirtualClassroom: A Lecturer-Centered Consumer-Grade Immersive Teaching System in Cyber–Physical–Social Space

VirtualClassroom: A Lecturer-Centered Consumer-Grade Immersive Teaching System in Cyber–Physical–Social Space

Cognitive-Based Crack Detection for Road Maintenance: An Integrated System in Cyber-Physical-Social Systems

A New Parallel Intelligence Based Light Field Dataset for Depth Refinement and Scene Flow Estimation

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