Robust video tracking algorithm: a multi‐feature fusion approach

Wang, Howard; Nguang, Sing Kiong; Wen, Jiwei

doi:10.1049/iet-cvi.2017.0404

Cited by 14 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(1) Difficulty in isolating areas of the human body in a rapidly changing environment: under ideal lighting and simple background conditions, existing image segmentation methods can extract human regions well. However, when the light is too bright or too dark and the background is complex, it is difficult for the image segmentation method based on monocular or binocular camera to accurately extract the human body area [10,11] (2) The problem of self-recognition in the process of human gesture recognition…”

Section: Multifeature Fusion Human Pose Tracking Algorithm Based On M...mentioning

confidence: 99%

Multifeature Fusion Human Pose Tracking Algorithm Based on Motion Image Analysis

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Human pose and motion detection is an important area of computer vision research, covering the interplay of different fields such as image processing, pattern recognition, and artificial intelligence. Due to the complexity of human motion, existing 3D recognition and pose detection methods based on low-quality depth images are not very accurate and reliable. Due to the time-sensitive nature of features, a single feature cannot adapt to the dynamic changes of the scene, so it is difficult for the target tracking algorithm based on a single feature to achieve robust tracking results. If multiple features are fused and applied in the tracking algorithm, the complementarity between different features can be used to better adapt to the scene changes and achieve robust tracking results. In order to solve the problem of human pose and human motion recognition in low-quality depth images, this paper uses the Kinect somatosensory camera to obtain 20 human skeleton joint points through the Kinect skeleton tracking technology. This paper studies the typical human posture and interactive action recognition technology in daily life. On the basis of understanding the characteristics of skeletal data, this paper proposes a distance feature and angle feature model combined with human body structure. Through the experimental results, it is found that the distance feature and the angle feature value are basically not affected by the distance change. When the subjects turned 45° to the left and 45° to the right, the distance characteristics changed, which was different from the characteristic data when the subjects turned around.

show abstract

Section: Multifeature Fusion Human Pose Tracking Algorithm Based On M...mentioning

confidence: 99%

Multifeature Fusion Human Pose Tracking Algorithm Based on Motion Image Analysis

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…The object tracking algorithms based on CF [6,7] convert the tracking operations into the frequency domain, which can reduce the amount of calculation and ensure data integrity. Many domestic and foreign scholars have introduced CF into object tracking algorithms IASC, 2023 3 [8,9] and achieved excellent results in the latest open datasets and academic competitions. The trackers based on CF only need to extract features from the searching box once and generate many candidate samples through cyclic convolution operation.…”

Section: Tracking By Correlation Filtersmentioning

confidence: 99%

Object Tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals

Sun¹,

Li²,

Cheng³

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion, a tracking algorithm based on multi-time-space perception and instancespecific proposals is proposed to optimize the mathematical model of the correlation filter (CF). Firstly, according to the consistency of the changes between the object frames and the filter frames, the mask matrix is introduced into the objective function of the filter, so as to extract the spatio-temporal information of the object with background awareness. Secondly, the object function of multi-feature fusion is constructed for the object location, which is optimized by the Lagrange method and solved by closed iteration. In the process of filter optimization, the constraints term of time-space perception is designed to enhance the learning ability of the CF to optimize the final tracking results. Finally, when the tracking results fluctuate, the boundary suppression factor is introduced into the instance-specific proposals to reduce the risk of model drift effectively. The accuracy and success rate of the proposed algorithm are verified by simulation analysis on two popular benchmarks, the object tracking benchmark 2015 (OTB2015) and the temple color 128 (TC-128). Extensive experimental results illustrate that the optimized appearance model of the proposed algorithm is effective. The distance precision rate and overlap success rate of the proposed algorithm are 0.756 and 0.656 on the OTB2015 benchmark, which are better than the results of other competing algorithms. The results of this study can solve the problem of real-time object tracking in the real traffic environment and provide a specific reference for the detection of traffic abnormalities.

show abstract

“…It distinguishes videos by manually marking some text descriptions or numbers, and when searching videos, it uses marked labels to search. Literature [25] reveals that, according to research, a large number of domestic and international scientific research institutions and related scholars are dedicated to the study of motion recognition based on video and have made significant contributions to the field's development. Domestic motion recognition research began relatively late, according to [26], using the big data analysis method.…”

Section: Related Workmentioning

confidence: 99%

High-Dynamic Dance Motion Recognition Method Based on Video Visual Analysis

Luo

Ning

2022

Scientific Programming

View full text Add to dashboard Cite

In the field of computer vision, high-dynamic dance motion recognition is a difficult problem to solve. Its goal is to recognize human motion by analyzing video data using image processing and classification recognition technology. Video multifeature fusion has sparked a surge in research in a variety of fields. Several pixel points that can be distinguished and displayed in several adjacent images that can reflect their characteristics are referred to as multifeature fusion. It is responsible for a significant portion of the similarity results between the two video segments. Motion recognition relies heavily on video multifeature fusion, which has a direct impact on the robustness and accuracy of recognition results. The directional gradient histogram features, optical flow direction histogram features, and audio features extracted from dance video are used to characterize dance movements after all of the characteristics of dance movements have been considered. This paper focuses on the high-dynamic dance action recognition method based on video multifeature fusion, which aims to combine high-dynamic dance action recognition and video multifeature fusion.

show abstract

Robust video tracking algorithm: a multi‐feature fusion approach

Cited by 14 publications

References 29 publications

Multifeature Fusion Human Pose Tracking Algorithm Based on Motion Image Analysis

Multifeature Fusion Human Pose Tracking Algorithm Based on Motion Image Analysis

Object Tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals

High-Dynamic Dance Motion Recognition Method Based on Video Visual Analysis

Contact Info

Product

Resources

About