Objave

@inproceedings{kristan2018sixth,

title = {The sixth visual object tracking VOT2018 challenge results},

author = {Matej Kristan and Ales Leonardis and Jiri Matas and Michael Felsberg and Roman Pflugfelder and Luka Cehovin Zajc and Tomas Vojir and Goutam Bhat and Alan Lukezic and Abdelrahman Eldesokey and Vitomir Štruc and Klemen Grm and others},

url = {http://openaccess.thecvf.com/content_ECCVW_2018/papers/11129/Kristan_The_sixth_Visual_Object_Tracking_VOT2018_challenge_results_ECCVW_2018_paper.pdf},

year  = {2018},

date = {2018-09-01},

booktitle = {European Conference on Computer Vision Workshops (ECCV-W 2018)},

abstract = {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 longterm 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.},

keywords = {benchmark, tracking, VOT},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{vidal2018ug,

title = {UG^ 2: A Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition},

author = {Rosaura G. Vidal and Sreya Banerjee and Klemen Grm and Vitomir Struc and Walter J. Scheirer},

url = {https://arxiv.org/pdf/1710.02909.pdf},

year  = {2018},

date = {2018-02-01},

booktitle = {2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},

pages = {1597--1606},

organization = {IEEE},

abstract = {Advances in image restoration and enhancement techniques have led to discussion about how such algorithms can be applied as a pre-processing step to improve automatic visual recognition. In principle, techniques like deblurring and super-resolution should yield improvements by de-emphasizing noise and increasing signal in an input image. But the historically divergent goals of computational photography and visual recognition communities have created a significant need for more work in this direction. To facilitate new research, we introduce a new benchmark dataset called UG2, which contains three difficult real-world scenarios: uncontrolled videos taken by UAVs and manned gliders, as well as controlled videos taken on the ground. Over 150,000 annotated frames for hundreds of ImageNet classes are available, which are used for baseline experiments that assess the impact of known and unknown image artifacts and other conditions on common deep learning-based object classification approaches. Further, current image restoration and enhancement techniques are evaluated by determining whether or not they improve baseline classification performance. Results show that there is plenty of room for algorithmic innovation, making this dataset a useful tool going forward.},

keywords = {benchmark, computational photography, image enhancement, image restoration, UAV, UG2, visual recognition},

pubstate = {published},

tppubtype = {inproceedings}

}