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- José María MartínezSuggested from co-authorship
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Works64 from public data
- The Visual Object Tracking VOT2015 Challenge Results902
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 and presents a new VOT 2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute.
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
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 Visual Object Tracking VOT2016 Challenge Results776
The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
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- The Eighth Visual Object Tracking VOT2020 Challenge Results350
A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
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- Semantic-aware scene recognition134
A novel approach for scene recognition based on an end-to-end multi-modal CNN that combines image and context information by means of an attention module is described, which outperforms every other state-of-the-art method while significantly reducing the number of network parameters.
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- The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results66
A significant general improvement of results has been observed, which partly compensate for the more difficult sequences introduced into the dataset in VOT-TIR2016.
- People detection in surveillance: classification and evaluation31
This paper is a postprint of a paper submitted to and accepted for publication in IET Computer Vision and is subject to Institution of Engineering and Technology Copyright.
- Towards automatic waste containers management in cities via computer vision: containers localization and geo-positioning in city maps26
Experimental results indicate that the proposed visual model for waste container detection is able to effectively operate with consistent performance disregarding the container type and the city layout.
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- On collaborative people detection and tracking in complex scenarios22
The experimental results over an extensive and challenging video dataset point out the state of the art limitations in complex or realistic scenarios and show that the proposed collaborative system significantly reduces these limitations and improves the results in this kind of scenarios.
- A Review on Deep Learning Methods for Glioma Segmentation, Limitations, and Future Perspectives20
A comparison of model performance on the BraTS datasets benchmark is presented and a suitability analysis for top-performing models based on their robustness, efficiency, and completeness of tumor region delineation are introduced.
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- A corpus for benchmarking of people detection algorithms10
A corpus, dataset and associated ground-truth, for the evaluation of people detection algorithms in surveillance video scenarios, along with the design procedure followed to generate it, is described.
- Synthmanticlidar: A Synthetic Dataset For Semantic Segmentation On Lidar Imaging9
This work presents a modified CARLA simulator designed with LiDAR semantic segmentation in mind, with new classes, more consistent object labeling with their counterparts from real datasets such as SemanticKITTI, and the possibility to adjust the object class distribution.
- Exploiting semantic segmentation to boost reinforcement learning in video game environments8
This work proves that using semantic segmentation enables reaching higher performance than the baseline reinforcement learning algorithm without modifying the actual algorithm, and in fewer episodes; it shows noticeable performance improvements when training on multiple levels at the same time; and it allows to apply transfer learning for models trained on visually different environments.
- Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information †8
A coarse-to-fine framework to adapt frame by frame people detectors during runtime classification, without requiring any additional manually labeled ground truth apart from the offline training of the detection model, is proposed.
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- On exploring weakly supervised domain adaptation strategies for semantic segmentation using synthetic data7
This paper proposes a synthetic data generation protocol, which identifies key features affecting performance and provides datasets with variable complexity, and proposes a new curriculum learning strategy to employ several synthetic and real datasets.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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