A polar model for fast object tracking in 360-degree camera images

Delforouzi, Ahmad; Tabatabaei, Seyed Amir Hossein; Shirahama, Kimiaki; Grzegorzek, Marcin

doi:10.1007/s11042-018-6525-0

Cited by 4 publications

(5 citation statements)

References 33 publications

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“…In this way, it boosted the overall speed of the algorithm with no effect on tracking performance. Figure 11 shows a comparison of our method's speed performance with the performances presented by Ahmad et al [18]. These methods also presented simple online arbitrary object trackers by using the conventional approaches in their techniques for fast object tracking in 360 • videos.…”

Section: Speed Evaluationmentioning

confidence: 91%

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Real-Time Application for Generating Multiple Experiences from 360° Panoramic Video by Tracking Arbitrary Objects and Viewer’s Orientations

Shah¹,

Han²,

Lee³

2020

Applied Sciences

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We propose a novel authoring and viewing system for generating multiple experiences with a single 360° video and efficiently transferring these experiences to the user. An immersive video contains much more interesting information within the 360° environment than normal videos. There can be multiple interesting areas within a 360° frame at the same time. Due to the narrow field of view in virtual reality head-mounted displays, a user can only view a limited area of a 360° video. Hence, our system is aimed at generating multiple experiences based on interesting information in different regions of a 360° video and efficient transferring of these experiences to prospective users. The proposed system generates experiences by using two approaches: (1) Recording of the user’s experience when the user watches a panoramic video using a virtual reality head-mounted display, and (2) tracking of an arbitrary interesting object in a 360° video selected by the user. For tracking of an arbitrary interesting object, we have developed a pipeline around an existing simple object tracker to adapt it for 360° videos. This tracking algorithm was performed in real time on a CPU with high precision. Moreover, to the best of our knowledge, there is no such existing system that can generate a variety of different experiences from a single 360° video and enable the viewer to watch one 360° visual content from various interesting perspectives in immersive virtual reality. Furthermore, we have provided an adaptive focus assistance technique for efficient transferring of the generated experiences to other users in virtual reality. In this study, technical evaluation of the system along with a detailed user study has been performed to assess the system’s application. Findings from evaluation of the system showed that a single 360° multimedia content has the capability of generating multiple experiences and transfers among users. Moreover, sharing of the 360° experiences enabled viewers to watch multiple interesting contents with less effort.

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Section: Speed Evaluationmentioning

confidence: 91%

“…However, the processing speed of their framework did not meet the requirement of being in real time. In 2018, Ahmad et al made refinements to their previous work presented in [16] and designed an enhanced polar model [18] for fast object tracking in polar sequences. Still, with a speed of 9 fps, they could not get much closer to real time.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Application for Generating Multiple Experiences from 360° Panoramic Video by Tracking Arbitrary Objects and Viewer’s Orientations

Shah¹,

Han²,

Lee³

2020

Applied Sciences

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“…In solving the filter L(x), it is described as a regularized least-squares objective function in the form of the tangent function as in (6), where l(x) is the filter associated with the x-th frame, l(x) n denotes the filter corresponding to each feature dimension, g(x) d is the feature map corresponding to each feature in the input candidate frame, and n � l, 2,..., N, N is the number of feature dimensions and takes the value of 10.…”

Section: Panoramic Video Multitarget Real-time Trackingmentioning

confidence: 99%

“…Currently, most tracking algorithms are trained, tested, and evaluated online with parameters based on publicly available datasets, and the target tracking models trained in this way show good tracking performance on certain datasets [5]. However, the ultimate task of target tracking algorithms is to deal with target tracking problems in real application scenarios, and the actual tracking environment is subject to unpredictable interference factors at any time, which leads to complex and variable tracking scenarios and eventually affects the tracking results of the algorithms [6]. Currently, target tracking in complex scenarios such as target occlusion, illumination change, deformation, low resolution, low illumination, and rotation may still cause tracking drift or loss due to interference in the target appearance representation model, weak model discrimination, or incorrect model update, and tracking accuracy and robustness still need to be further improved to continuously meet the needs of practical applications [7].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Multitarget Tracking for Panoramic Video Based on Dual Neural Networks for Multisensor Information Fusion

Lin

2022

Mathematical Problems in Engineering

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A multitarget real-time tracking system for panoramic video with multisensor information fusion dual neural networks is studied and implemented by combining dual neural networks, fused geometric features, and deep learning-based target real-time tracking algorithms. The motion model of multisensor information fusion is analyzed, the dual neural network perturbation model is introduced, and the state variables of the system are determined. Combined with the data structure model of multisensor information fusion, a least-squares-based optimization function is constructed. In this optimization function, the image sensor is constructed as an observationally constrained residual term based on the pixel plane, the inertial measurement unit is constructed as an observationally constrained residual term based on the motion model, and the motion relationship between sensors is constructed as a motion-constrained residual term to derive a mathematical model for real-time multitarget tracking of panoramic video. The simulation experimental results show that the integrated position estimation accuracy of the multisensor information fusion dual neural network-based panoramic video multitarget real-time tracking fusion algorithm is 10.5% higher than that of the optimal distributed panoramic video multitarget fusion algorithm without feedback. The results of the simulation experiments show that the integrated position estimation accuracy of the multitarget real-time tracking fusion algorithm for panoramic video improves by 10.96%, and the integrated speed estimation accuracy improves by 6.32% compared with the optimal distributed multitarget fusion algorithm without feedback. The proposed panoramic video multitarget real-time tracking algorithm based on the dual neural network can effectively improve the target tracking accuracy of the model on degraded frames (motion blur, target occlusion, out-of-focus, etc.), and the stability of the algorithm for target location and category detection is effectively improved by multiframe feature fusion. The research in this paper provides better technical support and research theory for panoramic video multitarget real-time tracking.

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“…The first detects each person by an object detector, and the second associates the cross-frame identities by considering their appearance similarity and trajectory trend [7,8]. Most existing studies work on 2D/3D single-view [2,5], or, 2D panoramic multi-target tracking [9,10]. However, previous works have limitations: the 2D tracking results could not be directly used in some applications (i.e., robotics) since the real-world coordinate is 3D; it is easy to lose the tracking target in a 3D narrow-angle-view coordinate since it only covers a part of the surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

Using Panoramic Videos for Multi-Person Localization and Tracking In A 3D Panoramic Coordinate

Yang

Wu³

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This work proposes a new human-related video processing task named 3D panoramic multi-person localization and tracking. With a benchmark dataset and a simple yet effective solution, it establishes a new paradigm for multi-person tracking systems and related applications. Unlike existing methods that can only work on a 2D coordinate or a narrowangle-view 3D coordinate, our proposal can maximally explore the 3D trajectory information of tracking targets. This is approached by applying camera geometry to transform human locations from 2D panoramic image coordinates to a 3D panoramic camera coordinate, and then by applying a tracking algorithm that associates human appearance and 3D trajectory together. Our code and dataset will be available at https://github.com/fandulu/MPLT.

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A polar model for fast object tracking in 360-degree camera images

Cited by 4 publications

References 33 publications

Real-Time Application for Generating Multiple Experiences from 360° Panoramic Video by Tracking Arbitrary Objects and Viewer’s Orientations

Real-Time Application for Generating Multiple Experiences from 360° Panoramic Video by Tracking Arbitrary Objects and Viewer’s Orientations

Real-Time Multitarget Tracking for Panoramic Video Based on Dual Neural Networks for Multisensor Information Fusion

Using Panoramic Videos for Multi-Person Localization and Tracking In A 3D Panoramic Coordinate

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