Journal Article10.1007/S11042-012-0994-3
Efficient tracking using a robust motion estimation technique
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TL;DR: A robust tracking approach based on object flow, which is a motion model for estimating both the displacement and the direction of an object of interest is proposed, yielding improved performance in comparison with other tracking approaches.
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Abstract: Camera based supervision is a critical part of event detection and analysis applications. However, visual tracking still remains one of the biggest challenges in the area of computer vision, although it has been extensively discussed during in the previous years. In this paper we propose a robust tracking approach based on object flow, which is a motion model for estimating both the displacement and the direction of an object of interest. In addition, an observation model that utilizes a generative prior is adopted to tackle the pitfalls that derive from the appearance changes of the object under study. The efficiency of our technique is demonstrated using sequences captured in a complex industrial environment. The experimental results show that the proposed algorithm is sound, yielding improved performance in comparison with other tracking approaches.
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Citations
Semi-supervised deep learning for object tracking and classification
Nikolaos Doulamis,Anastasios Doulamis +1 more
- 01 Oct 2014
TL;DR: A semi-supervised deep learning paradigm is proposed for object classification/tracking, by allowing unsupervised data to initially configure the network and then a gradient descent optimization scheme is triggered to fine tune the data.
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FAST-MDL: Fast Adaptive Supervised Training of multi-layered deep learning models for consistent object tracking and classification
Nikolaos Doulamis,Athanasios Voulodimos +1 more
- 01 Oct 2016
TL;DR: The proposed Fast Adaptive Supervised Training Algorithm, called FAST-MDL, provides promising results in consistent long term object labeling and detection under abruptly changing visual conditions, severe illumination changes and occlusions.
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A Threefold Dataset for Activity and Workflow Recognition in Complex Industrial Environments
Athanasios Voulodimos,Dimitrios Kosmopoulos,Georgios Vasileiou,Emmanuel Sardis,Vasileios Anagnostopoulos,Constantinos Lalos,Anastasios Doulamis,Theodora Varvarigou +7 more
TL;DR: The Workflow Recognition (WR) large-scale dataset is a collection of video sequences from the real industrial manufacturing environment of a major automobile manufacturer.
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Detecting and tracking dim small targets in infrared image sequences under complex backgrounds
TL;DR: In this paper, a unified framework for automatically detecting and tracking dim small targets in infrared (IR) image sequence under complex backgrounds is presented. And target tracking is accomplished by using the mean shift algorithm.
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Man overboard event detection from RGB and thermal imagery: possibilities and limitations
Iason Katsamenis,Eftychios Protopapadakis,Athanasios Voulodimos,D. Dres,Dimitris Drakoulis +4 more
- 30 Jun 2020
TL;DR: This paper investigates the possibilities as well as the limitations of man overboard vision-based systems' development based on RGB and thermal imagery and proposes a coherent methodology for fall detection over multiple sensors on a large-scale deployment.
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