Auto-correct-integrated trackers with and without memory of first frames

Visual short-term tracking in a long sequence of images with a lot of pose variations, target deformations and different types of occlusion is one of the harshest tasks in image processing. Overcoming such challenges can be performed in a superior way by combining different trackers. In this paper,...

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Veröffentlicht in:International journal of intelligent robotics and applications Online Jg. 4; H. 2; S. 191 - 201
Hauptverfasser: Sekhavati, Ali, Eghbal, Najmeh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Singapore 01.06.2020
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Artificial Intelligence
Computer Science
Computer vision
Control
Deep learning
Electronics and Microelectronics
Error correction
Fourier transforms
Image processing
Instrumentation
Machines
Manufacturing
Mechatronics
Neural networks
Object recognition
Occlusion
Optical tracking
Pattern recognition
Processes
Regular Paper
Robotics
Semantics
System theory
User Interfaces and Human Computer Interaction
Visual object tracking
Combined trackers
Self-correcting tracking
Correlation filters
ISSN:2366-5971, 2366-598X
Online-Zugang:Volltext
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Zusammenfassung:Visual short-term tracking in a long sequence of images with a lot of pose variations, target deformations and different types of occlusion is one of the harshest tasks in image processing. Overcoming such challenges can be performed in a superior way by combining different trackers. In this paper, we propose a method that combines different algorithms for tracking the given target. The algorithms are KCF (Henriques et al. in IEEE Trans Pattern Anal Mach Intell 37(3):583–596, 2014), MIL (Babenko et al. in Visual tracking with online multiple instance learning. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, 2009), CSR-DCF (Lukezic et al. in Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017) and MOSSE (Bolme et al. in Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010) trackers. In this method, these algorithms can continuously correct each other’s faults. They also implicitly search for the target even when they are tracking it, to make sure the object they are tracking is the real target. Thus, they outperform many of the state-of-the-art trackers as well as their components when they work independently. In this paper, we examine three trackers which we made in such way. Two of these trackers run at speeds close to real-time on a CPU and so we compare them with some famous wide-spread tracking algorithms both in terms of accuracy and speed.
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ISSN:2366-5971
2366-598X
DOI:10.1007/s41315-020-00137-0