REAL-TIME OBJECT DETECTION AND TRACKING FOR INDUSTRIAL APPLICATIONS
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Abstract: Real-time tracking of complex 3D objects has been shown to be a challenging task for industrial applications where robustness, accuracy and run-time performance are of critical importance. This paper presents a fully automated object tracking system which is capable of overcoming some of the problems faced in industrial environments. This is achieved by combining a real-time tracking system with a fast object detection system for automatic initialization and re-initialization at run-time. This ensures robustness of object detection, and at the same time accuracy and speed of recursive tracking. For the initialization we build a compact representation of the object of interest using statistical learning techniques during an off-line learning phase, in order to achieve speed and reliability at run-time by imposing geometric and photometric consistency constraints. The proposed tracking system is based on a novel template management algorithm which is incorporated into the ESM algorithm. Experimental results demonstrate the robustness and high precision of tracking of complex industrial machines with poor textures under severe illumination conditions. 1
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Deep learning-based approaches show great advantages in surface defect detection and other industrial applications.
“deep learning-based approaches show great advantages in both surface defects detection and other industrial applications, such as the automotive industry [2], fruit classification [3], and object detection [4,5].”
From Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network (2020) · cites this paper as [5]
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · see citing works
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · see citing works
PCA-SIFT: a more distinctive representation for local image descriptors
Ke, Y., & Sukthankar, R. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315206 · Cited by 3,237 · see citing works
Robust Wide Baseline Stereo from Maximally Stable Extremal Regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2002) · Procedings of the British Machine Vision Conference 2002 · doi:10.5244/c.16.36 · Cited by 1,350 · see citing works
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Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network
Lv, X., Duan, F., Jiang, J.-J., Fu, X., & Gan, L. (2020) · Sensors · doi:10.3390/s20061562 · Cited by 591 · see citing works
“detection and other industrial applications, such as the automotive industry [2], fruit classification [3], and object detection [4,5].”
Background
- Authors
- Benhimane, S., Najafi, H., Grundmann, M., Genç, Y., Navab, N., & Malis, E.
- Affiliations
- Technical University of Munich (DE)
- Siemens (United States) (US)
- Georgia Institute of Technology (US)
- Venue
- Proceedings of the Third International Conference on Computer Vision Theory and Applications
- Published
- 2008
- Pages
- 337-345
- OpenAlex
- W24440715
- Cited by
- 14
- References
- 4
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 1 · 2020: 4 · 2018: 2 · 2016: 1
- FWCI
- 0.80
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- International Conference on Computer Vision Theory and Applications (publishedVersion, cc-by-nc-nd)
- HAL (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- real-time object tracking, object detection, industrial vision, statistical learning, poor lighting conditions
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