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The Matrioska Tracking Algorithm on LTDT2014 Dataset

Mario Edoardo Maresca, Alfredo Petrosino
2014 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops  
We present a quantitative evaluation of Matrioska, a novel framework for the detection and tracking in realtime of unknown object in a video stream, on the LTDT2014 dataset that includes six sequences  ...  Matrioska follows the approach of tracking by detection: the detector localizes the target object in each frame, using multiple keypoint-based methods.  ...  The Matrioska Tracking Algorithm on LTDT2014 Dataset Mario Edoardo Maresca and Alfredo Petrosino Department of Science and Technology, University of Naples Parthenope, Centro Direzionale 80143, Napoli,  ... 
doi:10.1109/cvprw.2014.128 dblp:conf/cvpr/MarescaP14 fatcat:t54swoxpdjaq7n3vzkr6sdzydm

The Visual Object Tracking VOT2014 Challenge Results [chapter]

Matej Kristan, Roman Pflugfelder, Aleš Leonardis, Jiri Matas, Luka Čehovin, Georg Nebehay, Tomáš Vojíř, Gustavo Fernández, Alan Lukežič, Aleksandar Dimitriev, Alfredo Petrosino, Amir Saffari (+45 others)
2015 Lecture Notes in Computer Science  
The number of tested trackers makes VOT 2015 the largest benchmark on shortterm tracking to date. For each participating tracker, a short description is provided in the appendix.  ...  The dataset, the evaluation kit as well as the results are publicly available at the challenge website 1 .  ...  Acknowledgements This work was supported in part by the following research programs and projects: Slovenian research agency research programs P2-0214, P2-0094, Slovenian research agency projects J2-4284  ... 
doi:10.1007/978-3-319-16181-5_14 fatcat:oawrb5vmxvdeji675oyluf3dym

The Visual Object Tracking VOT2017 Challenge Results

Matej Kristan, Ales Leonardis, Jiri Matas, Michael Felsberg, Roman Pflugfelder, Luka Cehovin Zajc, Tomas Vojir, Gustav Hager, Alan Lukezic, Abdelrahman Eldesokey, Gustavo Fernandez, Alvaro Garcia-Martin (+92 others)
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
Acknowledgements This work was supported in part by the following research programs and projects: Slovenian research agency research programs P2-0214, P2-0094, Slovenian research agency projects J2-4284  ...  By default, MatFlow uses the trajectory given by Matrioska. In the case of a low confidence score estimated by Matrioska, the algorithm corrects the trajectory with the response given by BDF.  ...  Matrioska is likely to fail), the algorithm will use the trajectory given by BDF that is not sensitive to low textured objects. A.8.  ... 
doi:10.1109/iccvw.2017.230 dblp:conf/iccvw/KristanLMFPZVHL17 fatcat:3ik6smlk2belrhxick2vsn5rbu