Scale estimation for KCF tracker based on feature fusion
Pages 32 - 36
Abstract
Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, the visual tracking is still a tough problem due to background clutter, scale transformation and partial occlusions. In this paper, this paper proposes a novel tracker based on the KCF tracker. Firstly, the features including HoG and Color Name are fused together to heighten the overall tracking performance. Moreover, an effective scale estimation scheme is proposed to tackle the problem of the fixed template size in KCF tracker. The extensive empirical evaluations on the OTB demonstrate that the proposed tracker is very promising for the various challenging scenes. The target tracking algorithm proposed in this paper has accurate rate of 0.830 and success rate of 0.7.
References
[1]
Haritaoglu I, Harwood D, Davis L S. W4: Real-Time Surveillance of People and Their Activities[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, 22(8):809--830.
[2]
Siebel NT, Maybank SJ. The advisor visual surveillance system[C]. Proceedings of ECCV 2004 Workshop "Applications of Computer Vision ". Prague, Czech Republic. 2004. 103--111.
[3]
Rios-Cabrera R, Tuytelaars T, Gool L V. Efficient multicamera vehicle detection, tracking, and identification in a tunnel surveillance application[M]. Computer Vision and Image Understanding, 2012, 116(6): 742--753.
[4]
Hilsmann A, Eisert P. Deformable object tracking using optical flow constraints[C]. European Conference on Visual Media Production. IET, 2008.
[5]
Huang C M, Hung M H. Target motion compensation with optical flow clustering during visual tracking[C]. IEEE International Conference on Networking. IEEE, 2014.
[6]
F. Gui-Xia, G. Ming-Liang, Z. Guo-Feng, et al. An improved particle filter based on cuckoo search for visual tracking[C]. 2018 Chinese Control And Decision Conference (CCDC), Shenyang, 2018: 3687--3691.
[7]
T. Xi, S. Zhang and S. Yan. A Robust Visual Tracking Approach with Adaptive Particle Filtering[C]. 2010 Second International Conference on Communication Software and Networks, Singapore, 2010: 549--553.
[8]
Chen K, Fu S, Song K, et al. A Meanshift-based imbedded computer vision system design for real-time target tracking[C]. International Conference on Computer Science & Education. IEEE, 2012.
[9]
Kalal Z, Mikolajczyk K, Matas J. Tracking-Learning-Detection[J]. IEEE Trans Pattern Anal Mach Intell, 2012, 34(7):1409--1422.
[10]
Bai Y, Tang M. Robust visual tracking via ranking SVM[J]. 2011.
[11]
Zhang L, Varadarajan J, Suganthan P N, et al. Robust Visual Tracking Using Oblique Random Forests[C]. Computer Vision & Pattern Recognition. IEEE, 2017.
[12]
Bolme D S, Beveridge J R, Draper B A, et al. Visual object tracking using adaptive correlation filters[C]. The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2010.
[13]
Henriques J F, Caseiro R, Martins P, et al. High-Speed Tracking with Kernelized Correlation Filters[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3):583--596.
[14]
Danelljan M, Khan F S, Felsberg M, et al. Adaptive Color Attributes for Real-Time Visual Tracking[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2014.
[15]
Li Y, Zhu J. A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration[C]. European Conference on Computer Vision. Springer, Cham, 2014.
[16]
Danelljan M, Häger G, Khan F S, et al. Discriminative Scale Space Tracking[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(8):1561--1575.
[17]
Ma C, Yang X, Zhang C, et al. Long-term correlation tracking[C]. Computer Vision & Pattern Recognition. 2015.
[18]
Wu Y, Lim J, Yang M H. Object Tracking Benchmark[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(9):1834--1848.
Index Terms
- Scale estimation for KCF tracker based on feature fusion
Recommendations
An RGBD Tracker Based on KCF Adaptively Handling Long-Term Occlusion
Intelligent Computing Theories and ApplicationAbstractSince occlusion still be a challenge for object tracking in RGB data. In this paper, we propose an RGBD single-object tracker that built upon the well-known base KCF tracker and exploit how the depth information fusing to handle partial and long-...
Scale Adaptive Dense Structural Learning for Visual Object Tracking
ICCAE 2018: Proceedings of the 2018 10th International Conference on Computer and Automation EngineeringObject tracking has long been a hot topic in computer vision. However, existing trackers are still too far away from solving the visual tracking problem because of their limited robustness, inadequate precision and low efficiency. The correlation ...
Quadtree-based eigendecomposition for pose estimation in the presence of occlusion and background clutter
Eigendecomposition-based techniques are popular for a number of computer vision problems, e.g., object and pose estimation, because they are purely appearance based and they require few on-line computations. Unfortunately, they also typically require an ...
Comments
Information & Contributors
Information
Published In
May 2020
271 pages
ISBN:9781450376587
DOI:10.1145/3390557
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- The Hong Kong Polytechnic: The Hong Kong Polytechnic University
- Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 June 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICIAI 2020
ICIAI 2020: 2020 the 4th International Conference on Innovation in Artificial Intelligence
May 8 - 11, 2020
Xiamen, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 47Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 31 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in