Lidé

Ing. Tomáš Vojíř, Ph.D.

Všechny publikace

Calibrated Out-of-Distribution Detection with a Generic Representation

  • DOI: 10.1109/ICCVW60793.2023.00485
  • Odkaz: https://doi.org/10.1109/ICCVW60793.2023.00485
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    Out-of-distribution detection is a common issue in deploying vision models in practice and solving it is an essential building block in safety critical applications. Most of the existing OOD detection solutions focus on improving the OOD robustness of a classification model trained exclusively on in-distribution (ID) data. In this work, we take a different approach and propose to leverage generic pre-trained representation. We propose a novel OOD method, called GROOD, that formulates the OOD detection as a Neyman-Pearson task with well calibrated scores and which achieves excellent performance, predicated by the use of a good generic representation. Only a trivial training process is required for adapting GROOD to a particular problem. The method is simple, general, efficient, calibrated and with only a few hyper-parameters. The method achieves state-of-the-art performance on a number of OOD benchmarks, reaching near perfect performance on several of them. The source code is available at https://github.com/vojirt/GROOD.

Image-Consistent Detection of Road Anomalies As Unpredictable Patches

  • DOI: 10.1109/WACV56688.2023.00545
  • Odkaz: https://doi.org/10.1109/WACV56688.2023.00545
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    We propose a novel method for anomaly detection primarily aiming at autonomous driving. The design of the method, called DaCUP (Detection of anomalies as Consistent Unpredictable Patches), is based on two general properties of anomalous objects: an anomaly is (i) not from a class that could be modelled and (ii) it is not similar (in appearance) to non-anomalous objects in the image. To this end, we propose a novel embedding bottleneck in an auto-encoder like architecture that enables modelling of a diverse, multi-modal known class appearance (e.g. road). Secondly, we introduce novel image-conditioned distance features that allow known class identification in a nearest-neighbour manner on-the-fly, greatly increasing its ability to distinguish true and false positives. Lastly, an inpainting module is utilized to model the uniqueness of detected anomalies and significantly reduce false positives by filtering regions that are similar, thus reconstructable from their neighbourhood. We demonstrate that filtering of regions based on their similarity to neighbour regions, using e.g. an inpainting module, is general and can be used with other methods for reduction of false positives. The proposed method is evaluated on several publicly available datasets for road anomaly detection and on a maritime benchmark for obstacle avoidance. The method achieves state-of-the-art performance in both tasks with the same hyper-parameters with no domain specific design.

Efficient Large-Scale Semantic Visual Localization in 2D Maps

  • Autoři: Ing. Tomáš Vojíř, Ph.D., Budvytis, I., Cipolla, R.
  • Publikace: ACCV2020: Proceedings of the 15th Asian Conference on Computer Vision - Part III. Cham: Springer, 2021. p. 273-288. LNCS. vol. 12624. ISSN 0302-9743. ISBN 978-3-030-69534-7.
  • Rok: 2021
  • DOI: 10.1007/978-3-030-69535-4_17
  • Odkaz: https://doi.org/10.1007/978-3-030-69535-4_17
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    With the emergence of autonomous navigation systems, image-based localization is one of the essential tasks to be tackled. However, most of the current algorithms struggle to scale to city-size environments mainly because of the need to collect large (semi-)annotated datasets for CNN training and create databases for test environment of images, key-point level features or image embeddings. This data acquisition is not only expensive and time-consuming but also may cause privacy concerns. In this work, we propose a novel framework for semantic visual localization in city-scale environments which alleviates the aforementioned problem by using freely available 2D maps such as OpenStreetMap. Our method does not require any images or image-map pairs for training or test environment database collection. Instead, a robust embedding is learned from a depth and building instance label information of a particular location in the 2D map. At test time, this embedding is extracted from a panoramic building instance label and depth images. It is then used to retrieve the closest match in the database. We evaluate our localization framework on two large-scale datasets consisting of Cambridge and San Francisco cities with a total length of drivable roads spanning 500 km and including approximately 110k unique locations. To the best of our knowledge, this is the first large-scale semantic localization method which works on par with approaches that require the availability of images at train time or for test environment database creation.

Performance Evaluation Methodology for Long-Term Single-Object Tracking

  • DOI: 10.1109/TCYB.2020.2980618
  • Odkaz: https://doi.org/10.1109/TCYB.2020.2980618
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition to maximize the analysis probing strength. The new measures outperform existing ones in interpretation potential and in better distinguishing between different tracking behaviors. We show that these measures generalize the short-term performance measures, thus linking the two tracking problems. Furthermore, the new measures are highly robust to temporal annotation sparsity and allow annotation of sequences hundreds of times longer than in the current datasets without increasing manual annotation labor. A new challenging dataset of carefully selected sequences with many target disappearances is proposed. A new tracking taxonomy is proposed to position trackers on the short-term/long-term spectrum. The benchmark contains an extensive evaluation of the largest number of long-term trackers and comparison to state-of-the-art short-term trackers. We analyze the influence of tracking architecture implementations to long-term performance and explore various redetection strategies as well as the influence of visual model update strategies to long-term tracking drift. The methodology is integrated in the VOT toolkit to automate experimental analysis and benchmarking and to facilitate the future development of long-term trackers.

Road Anomaly Detection by Partial Image Reconstruction with Segmentation Coupling

  • Autoři: Ing. Tomáš Vojíř, Ph.D., Šipka, T., Aljundi, R., Chumerin, N., Reino, D.O., prof. Ing. Jiří Matas, Ph.D.,
  • Publikace: ICCV2021: Proceedings of the International Conference on Computer Vision. Piscataway: IEEE, 2021. p. 15651-15660. ISSN 2380-7504. ISBN 978-1-6654-2812-5.
  • Rok: 2021
  • DOI: 10.1109/ICCV48922.2021.01536
  • Odkaz: https://doi.org/10.1109/ICCV48922.2021.01536
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    We present a novel approach to the detection of unknownobjects in the context of autonomous driving. The problemis formulated as anomaly detection, since we assume thatthe unknown stuff or object appearance cannot be learned.To that end, we propose a reconstruction module that can beused with many existing semantic segmentation networks,and that is trained to recognize and reconstruct road (driv-able) surface from a small bottleneck. We postulate thatpoor reconstruction of the road surface is due to areas thatare outside of the training distribution, which is a strong in-dicator of an anomaly. The road structural similarity erroris coupled with the semantic segmentation to incorporateinformation from known classes and produce final per-pixelanomaly scores. The proposed JSR-Net was evaluated onfour datasets, Lost-and-found, Road Anomaly, Road Obsta-cles, and FishyScapes, achieving state-of-art performanceon all, reducing the false positives significantly, while typ-ically having the highest average precision for wide rangeof operation points.

FuCoLoT – A Fully-Correlational Long-Term Tracker

  • Autoři: Lukežič, A., Zajc, L.Č., Ing. Tomáš Vojíř, Ph.D., prof. Ing. Jiří Matas, Ph.D., Kristan, M.
  • Publikace: ACCV 2018: Proceedings of the 14th Asian Conference on Computer Vision, Part II. Springer, 2019. p. 595-611. LNCS. vol. 11362. ISSN 0302-9743. ISBN 978-3-030-20889-9.
  • Rok: 2019
  • DOI: 10.1007/978-3-030-20890-5_38
  • Odkaz: https://doi.org/10.1007/978-3-030-20890-5_38
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    We propose FuCoLoT – a Fully Correlational Long-term Tracker. It exploits the novel DCF constrained filter learning method to design a detector that is able to re-detect the target in the whole image efficiently. FuCoLoT maintains several correlation filters trained on different time scales that act as the detector components. A novel mechanism based on the correlation response is used for tracking failure estimation. FuCoLoT achieves state-of-the-art results on standard short-term benchmarks and it outperforms the current best-performing tracker on the long-term UAV20L benchmark by over 19%. It has an order of magnitude smaller memory footprint than its best-performing competitors and runs at 15Â fps in a single CPU thread.

The sixth visual object tracking VOT2018 challenge results

  • Autoři: Kristan, M., Leonardis, A., prof. Ing. Jiří Matas, Ph.D., Felsberg, M., Ing. Tomáš Vojíř, Ph.D.,
  • Publikace: Computer Vision – ECCV 2018 Workshops. Basel: Springer, 2019. p. 3-53. Lecture Notes in Computer Science. vol. 11129. ISSN 0302-9743. ISBN 978-3-030-11008-6.
  • Rok: 2019
  • DOI: 10.1007/978-3-030-11009-3_1
  • Odkaz: https://doi.org/10.1007/978-3-030-11009-3_1
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).

Discriminative Correlation Filter Tracker with Channel and Spatial Reliability

  • DOI: 10.1007/s11263-017-1061-3
  • Odkaz: https://doi.org/10.1007/s11263-017-1061-3
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard feature sets, HoGs and colornames, the novel CSR-DCF method---DCF with channel and spatial reliability---achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs close to real-time on a CPU.

Discriminative Correlation Filter with Channel and Spatial Reliability

  • Autoři: Lukežic, A.L., Ing. Tomáš Vojíř, Ph.D., Cehovin Zajc, L.C.Z., prof. Ing. Jiří Matas, Ph.D., Kristan, M.K.
  • Publikace: CVPR 2017: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Press, 2017. p. 4847-4856. ISSN 1063-6919. ISBN 978-1-5386-0457-1.
  • Rok: 2017
  • DOI: 10.1109/CVPR.2017.515
  • Odkaz: https://doi.org/10.1109/CVPR.2017.515
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a novel learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This allows tracking of non-rectangular objects as well as extending the search region. Channel reliability reflects the quality of the learned filter and it is used as a feature weighting coefficient in localization. Experimentally, with only two simple standard features, HOGs and Colornames, the novel CSR-DCF method – DCF with Channel and Spatial Reliability – achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB. The CSR-DCF runs in real-time on a CPU.

The Visual Object Tracking VOT2017 challenge results

  • Autoři: Kristan, M., Leonardis, A., prof. Ing. Jiří Matas, Ph.D., Felsberg, M., Ing. Tomáš Vojíř, Ph.D., Nosková, J.
  • Publikace: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW 2017). Piscataway, NJ: IEEE, 2017. p. 1949-1972. ISSN 2473-9944. ISBN 978-1-5386-1034-3.
  • Rok: 2017
  • DOI: 10.1109/ICCVW.2017.230
  • Odkaz: https://doi.org/10.1109/ICCVW.2017.230
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).

A Novel Performance Evaluation Methodology for Single-Target Trackers

  • Autoři: Kristan, M., prof. Ing. Jiří Matas, Ph.D., Leonardis, A., Ing. Tomáš Vojíř, Ph.D., Pflugfelder, R., Fernandez, G., Nebehay, G., Porikli, F., Cehovin, L.
  • Publikace: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2016, 38(11), 2137-2155. ISSN 0162-8828.
  • Rok: 2016
  • DOI: 10.1109/TPAMI.2016.2516982
  • Odkaz: https://doi.org/10.1109/TPAMI.2016.2516982
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    This paper addresses the problem of single-target tracker performance evaluation.We consider the performance measures, the dataset and the evaluation system to be the most important components of tracker evaluation and propose requirements for each of them. The requirements are the basis of a new evaluation methodology that aims at a simple and easily interpretable tracker comparison. The ranking-based methodology addresses tracker equivalence in terms of statistical significance and practical differences. A fully-annotated dataset with per-frame annotations with several visual attributes is introduced. The diversity of its visual properties is maximized in a novel way by clustering a large number of videos according to their visual attributes. This makes it the most sophistically constructed and annotated dataset to date. A multi-platform evaluation system allowing easy integration of third-party trackers is presented as well. The proposed evaluation methodology was tested on the VOT2014 challenge on the new dataset and 38 trackers, making it the largest benchmark to date. Most of the tested trackers are indeed state-of-the-art since they outperform the standard baselines, resulting in a highly-challenging benchmark. An exhaustive analysis of the dataset from the perspective of tracking difficulty is carried out. To facilitate tracker comparison a new performance visualization technique is proposed.

Online adaptive hidden Markov model for multi-tracker fusion

  • DOI: 10.1016/j.cviu.2016.05.007
  • Odkaz: https://doi.org/10.1016/j.cviu.2016.05.007
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    In this paper, we propose a novel method for visual object tracking called HMMTxD. The method fuses observations from complementary out-of-the box trackers and a detector by utilizing a hidden Markov model whose latent states correspond to a binary vector expressing the failure of individual trackers. The Markov model is trained in an unsupervised way, relying on an online learned detector to provide a source of tracker-independent information for a modified Baum- Welch algorithm that updates the model w.r.t. the partially annotated data. We show the effectiveness of the proposed method on combination of two and three tracking algorithms. The performance of HMMTxD is evaluated on two standard benchmarks (CVPR2013 and VOT) and on a rich collection of 77 publicly available sequences. The HMMTxD outperforms the state-of-the-art, often significantly, on all data-sets in almost all criteria.

The thermal infrared visual object tracking VOT-TIR2016 challenge results

  • Autoři: Felsberg, M., Kristan, M., prof. Ing. Jiří Matas, Ph.D., Leonardis, A., Ing. Tomáš Vojíř, Ph.D.,
  • Publikace: Computer Vision – ECCV 2016 Workshops, Part II. Cham: Springer International Publishing, 2016. pp. 824-849. Lecture Notes in Computer Science. vol. 9914. ISSN 0302-9743. ISBN 978-3-319-48880-6.
  • Rok: 2016
  • DOI: 10.1007/978-3-319-48881-3_55
  • Odkaz: https://doi.org/10.1007/978-3-319-48881-3_55
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.

The visual object tracking VOT2016 challenge results

  • Autoři: Kristan, M., Leonardis, A., prof. Ing. Jiří Matas, Ph.D., Felsberg, M., Ing. Tomáš Vojíř, Ph.D.,
  • Publikace: Computer Vision – ECCV 2016 Workshops, Part II. Cham: Springer International Publishing, 2016. pp. 777-823. Lecture Notes in Computer Science. vol. 9914. ISSN 0302-9743. ISBN 978-3-319-48880-6.
  • Rok: 2016
  • DOI: 10.1007/978-3-319-48881-3_54
  • Odkaz: https://doi.org/10.1007/978-3-319-48881-3_54
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http: //votchallenge.net).

The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results

  • Autoři: Felsberg, M., Berg, A., Hager, G., Ahlberg, J., prof. Ing. Jiří Matas, Ph.D., Ing. Tomáš Vojíř, Ph.D.,
  • Publikace: The IEEE International Conference on Computer Vision (ICCV) Workshops. New York: IEEE Computer Society Press, 2015. pp. 639-651. ISSN 1550-5499. ISBN 978-1-4673-8390-5.
  • Rok: 2015
  • DOI: 10.1109/ICCVW.2015.86
  • Odkaz: https://doi.org/10.1109/ICCVW.2015.86
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Linkoping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.

The Visual Object Tracking VOT2014 Challenge Results

  • Autoři: Kristan, M., Pflugfelder, R., Leonardis, A., prof. Ing. Jiří Matas, Ph.D., Cehovin, L., Nebehay, G., Ing. Tomáš Vojíř, Ph.D., Fernandez, G.
  • Publikace: Computer Vision - ECCV 2014 Workshops, Part II. Cham: Springer, 2015. pp. 191-217. Lecture Notes in Computer Science. ISSN 0302-9743. ISBN 978-3-319-16180-8.
  • Rok: 2015
  • DOI: 10.1007/978-3-319-16181-5_14
  • Odkaz: https://doi.org/10.1007/978-3-319-16181-5_14
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    The Visual Object Tracking challenge 2014, VOT2014, aims at comparing sho rt-term single-object visual trackers that do not apply pre-learned models of object appea rance. Results of 38 trackers are presented. The number of tested trackers makes VOT 2014 the largest benchmark on short-term tracking to date. For each participating tracker, a sh ort description is provided in the appendix. Features of the VOT2014 challenge that go bey ond its VOT2013 predecessor are introduced: (i) a new VOT2014 dataset with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2 013 evaluation methodology, (iii) a new unit for tracking speed assessment less dependent on the hardware and (iv) the VOT2014 evaluation toolkit that significantly speeds up execu tion of experiments. The dataset, the evaluation kit as well as the results are publicly a vailable at the challenge website (http://www.votchallenge.net/).

The Visual Object Tracking VOT2015 challenge results

  • Autoři: Kristan, M., prof. Ing. Jiří Matas, Ph.D., Leonardis, A., Felsberg, M., Ing. Tomáš Vojíř, Ph.D.,
  • Publikace: The IEEE International Conference on Computer Vision (ICCV) Workshops. New York: IEEE Computer Society Press, 2015. pp. 564-586. ISSN 1550-5499. ISBN 978-1-4673-8390-5.
  • Rok: 2015
  • DOI: 10.1109/ICCVW.2015.79
  • Odkaz: https://doi.org/10.1109/ICCVW.2015.79
  • Pracoviště: Skupina vizuálního rozpoznávání
  • Anotace:
    The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website(1).

Robust scale-adaptive mean-shift for tracking

  • DOI: 10.1016/j.patrec.2014.03.025
  • Odkaz: https://doi.org/10.1016/j.patrec.2014.03.025
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    The mean-shift procedure is a popular object tracking algorithm since it is f ast, easy to implement and performs well in a range of conditions. We address the problem of s cale adaptation and present a novel theoretically justified scale estimation mechanism which relies solely on the mean-shift procedure for the Hellinger distance. We also propose two impro vements of the mean-shift tracker that make the scale estimation more robust in the presence of background clutter. The first one is a novel histogram color weighting that exploits the object neighborhood to help discriminate the target called background ratio weighting (BRW). We s how that the BRW improves performance of MS-like tracking methods in general. The second impro vement boost the performance of the tracker with the proposed scale estimation by the introduc tion of a forward-backward consistency check and by adopting regularization terms that counter two major problems: scale expansion caused by background clutter and scale implosion on self-similar objects. The proposed mean-shift tracker with scale selection and BRW is compared with recent state-of-the-art algorithms on a dataset of 77 public sequences. It outperforms the re ference algorithms in average recall, processing speed and it achieves the best score for 30% of the sequences - the highest percentage among the reference algorithms.

The Enhanced Flock of Trackers

  • DOI: 10.1007/978-3-642-44907-9_6
  • Odkaz: https://doi.org/10.1007/978-3-642-44907-9_6
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    The paper presents contributions to the design of the Flock of Trackers (FoT). The FoT estimates the pose of the tracked object by robustly combining displacement estimates from a subset of local trackers that cover the object and has been. The enhancements of the Flock of Trackers are: (i) new reliability predictors for the local trackers - the Neighbourhood consistency predictor and the Markov predictor, (ii) new rules for combining the predictions and (iii) introduction of a RANSAC-based estimator of object motion. The enhanced FoT was extensively tested on 62 sequences.Most of the sequences are standard and used in the literature. The improved FoT showed performance superior to the reference method. For all 62 sequences, the ground truth is made publicly available.

Robust Scale-Adaptive Mean-Shift for Tracking

  • Autoři: Ing. Tomáš Vojíř, Ph.D., Nosková, J., prof. Ing. Jiří Matas, Ph.D.,
  • Publikace: SCIA 2013: Proceedings of the 18th Scandinavian Conference on Image Analysis. Heidelberg: Springer, 2013. p. 652-663. Lecture Notes in Computer Science. ISSN 0302-9743. ISBN 978-3-642-38885-9.
  • Rok: 2013
  • DOI: 10.1007/978-3-642-38886-6_61
  • Odkaz: https://doi.org/10.1007/978-3-642-38886-6_61
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Mean-Shift tracking is a popular algorithm for object tracking since it is easy to implement and it is fast and robust. In this paper, we address the problem of scale adaptation of the Hellinger distance based Mean-Shift tracker. We start from a theoretical derivation of scale estimation in the Mean-Shift framework. To make the scale estimation robust and suitable for tracking, we in- troduce regularization terms that counter two major problem: (i) scale expansion caused by background clutter and (ii) scale implosion on self-similar objects. To further robustify the scale estimate, it is validated by a forward-backward consis- tency check. The proposed Mean-shift tracker with scale selection is compared with re- cent state-of-the-art algorithms on a dataset of 48 public color sequences and it achieved excellent results.

The Visual Object Tracking VOT2013 Challenge Results

  • Autoři: Kristan, M., Pflugfelder, R., Leonardis, A., prof. Ing. Jiří Matas, Ph.D., Porikli, F., Cehovin, L., Nebehay, G., Fernandez, G., Ing. Tomáš Vojíř, Ph.D.,
  • Publikace: IEEE International Conference on Computer Vision (ICCV 2013) Worskhops. Piscataway: IEEE, 2013. pp. 98-111. ISSN 1550-5499. ISBN 978-1-4799-3022-7.
  • Rok: 2013
  • DOI: 10.1109/ICCVW.2013.20
  • Odkaz: https://doi.org/10.1109/ICCVW.2013.20
  • Pracoviště: Katedra kybernetiky
  • Anotace:
    Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow he developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of ifferent tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (modelfree). Presented here is the OT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker enchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic omparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website1.

A System for Real-time Detection and Tracking of Vehicles from a Single Car-mounted Camera

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    A novel system for detection and tracking of vehicles from a single car-mounted camera is presented. The core of the system are high-performance vision algorithms: the WaldBoost detector and the TLD tracker that are scheduled so that a real-time performance is achieved. The vehicle monitoring system is evaluated on a new dataset collected on Italian motorways which is provided with approxi- mate ground truth (GT'') obtained from laser scans. For a wide range of distances, the recall and precision of detection for cars are excellent. Statistics for trucks are also reported. The dataset with the ground truth is made public.

Robustifying the Flock of Trackers

  • Pracoviště: Katedra kybernetiky
  • Anotace:
    The paper presents contributions to the design of the Flock of Trackers (FoT). The FoT trackers estimate the pose of the tracked object by robustly combining displacement estimates from local trackers that cover the object. The first contribution, called the Cell FoT, allows local trackers to drift to points good to track. The Cell FoT was compared with the Kalal et al. Grid FoT [4] and outperformed it on all sequences but one and for all local failure prediction methods. As a second contribution, we introduce two new predictors of local tracker failure - the neighbourhood consistency predictor (Nh) and the Markov predictor (Mp) and show that the new predictors combined with the NCC predictor are more powerful than the Kalal et al. [4] predictor based on NCC and FB. The resulting tracker equipped with the new predictors combined with the NCC predictor was compared with state-of-the-art tracking algorithms and surpassed them in terms of the number of sequences where a given tracking.

Za stránku zodpovídá: Ing. Mgr. Radovan Suk