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@article{ContexedNet_Emersic_2021,

title = {ContexedNet : Context-Aware Ear Detection in Unconstrained Settings},

author = {Žiga Emeršič and Diego Sušanj and Blaž Meden and Peter Peer and Vitomir Štruc},

editor = {ContexedNet : Context-Aware Ear Detection in Unconstrained Settings},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9583244},

issn = {2169-3536},

year  = {2021},

date = {2021-10-20},

urldate = {2021-10-20},

journal = {IEEE Access},

pages = {1--17},

abstract = {Ear detection represents one of the key components of contemporary ear recognition systems. While significant progress has been made in the area of ear detection over recent years, most of the improvements are direct results of advances in the field of visual object detection. Only a limited number of techniques presented in the literature are domain--specific and designed explicitly with ear detection in mind. In this paper, we aim to address this gap and present a novel detection approach that does not rely only on general ear (object) appearance, but also exploits contextual information, i.e., face--part locations, to ensure accurate and robust ear detection with images captured in a wide variety of imaging conditions. The proposed approach is based on a Context--aware Ear Detection Network (ContexedNet) and poses ear detection as a semantic image segmentation problem. ContexedNet consists of two processing paths: 1) a context--provider that extracts probability maps corresponding to the locations of facial parts from the input image, and 2) a dedicated ear segmentation model that integrates the computed probability maps into a context--aware segmentation-based ear detection procedure. ContexedNet is evaluated in rigorous experiments on the AWE and UBEAR datasets and shown to ensure competitive performance when evaluated against state--of--the--art ear detection models from the literature. Additionally, because the proposed contextualization is model agnostic, it can also be utilized with other ear detection techniques to improve performance.},

keywords = {biometrics, contextual information, deep leraning, ear detection, ear recognition, ear segmentation, neural networks, segmentation},

pubstate = {published},

tppubtype = {article}

}

@article{CrowdCountingPevec,

title = {Benchmarking Crowd-Counting Techniques across Image Characteristics},

author = {Klemen Pevec and Klemen Grm and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/10/Pevec.pdf

https://ev.fe.uni-lj.si/5-2021/Pevec.pdf},

year  = {2021},

date = {2021-05-01},

journal = {Elektorethniski Vestnik},

volume = {88},

issue = {5},

pages = {227-235},

abstract = {Crowd--counting is a longstanding computer vision used in estimating the crowd sizes for security purposes at public protests in streets, public gatherings, for collecting crowd statistics at airports, malls, concerts, conferences, and other similar venues, and for monitoring people and crowds during public health crises (such as the one caused by COVID-19). Recently, the performance of automated methods for crowd--counting from single images has improved particularly due to the introduction of deep learning techniques and large labelled training datasets. However, the robustness of these methods to varying imaging conditions, such as weather, image perspective, and large variations in the crowd size has not been studied in-depth in the open literature. To address this gap, a systematic study on the robustness of four recently developed crowd--counting methods is performed in this paper to evaluate their performance with respect to variable (real-life) imaging scenarios that include different event types, weather conditions, image sources and crowd sizes. It is shown that the performance of the tested techniques is degraded in unclear weather conditions (i.e., fog, rain, snow) and also on images taken from large distances by drones. On the opposite, clear weather conditions, crowd--counting methods can provide accurate and usable results.},

keywords = {CNN, crowd counting, drones, image characteristics, model comparison, neural networks},

pubstate = {published},

tppubtype = {article}

}