publications

2025

1. ICIP

   Texture- and Shape-Based Adversarial Attacks for Overhead Image Vehicle Detection

   Mikael Yeghiazaryan, Sai Abhishek Siddhartha Namburu, Emily Kim, Stanislav Panev, Celso De Melo, Fernando De La Torre, and Jessica K. Hodgins

   In 2025 IEEE International Conference on Image Processing (ICIP), 2025

   DOI
2. SPIE

   Leveraging generative AI for cross-regional small object detection in satellite imagery

   Zheyang Qin, Stanislav Panev, Celso De Melo, Shayok Chakraborty, Jessica Hodgins, and Fernando De La Torre

   In Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications III, 2025

   DOI
3. ICCV

   Adapting Vehicle Detectors for Aerial Imagery to Unseen Domains with Weak Supervision

   Xiao Fang, Minhyek Jeon, Zheyang Qin, Stanislav Panev, Celso De Melo, Shuowen Hu, Shayok Chakraborty, and Fernando De La Torre

   In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2025

2024

1. WACV

   Exploring the Impact of Rendering Method and Motion Quality on Model Performance When Using Multi-View Synthetic Data for Action Recognition

   Stanislav Panev^*, Emily Kim^*, Sai Abhishek Si Namburu, Desislava Nikolova, Celso De Melo, Fernando De La Torre, and Jessica Hodgins

   In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Jan 2024

2020

1. MDPI Sensors

   Parkinson’s Disease Tremor Detection in the Wild Using Wearable Accelerometers

   Rubén San-Segundo, Ada Zhang, Alexander Cebulla, Stanislav Panev, Griffin Tabor, Katelyn Stebbins, Robyn E. Massa, Andrew Whitford, Fernando De La Torre, and Jessica Hodgins

   Sensors, 2020

   Abs DOI

   

   Continuous in-home monitoring of Parkinson’s Disease (PD) symptoms might allow improvements in assessment of disease progression and treatment effects. As a first step towards this goal, we evaluate the feasibility of a wrist-worn wearable accelerometer system to detect PD tremor in the wild (uncontrolled scenarios). We evaluate the performance of several feature sets and classification algorithms for robust PD tremor detection in laboratory and wild settings. We report results for both laboratory data with accurate labels and wild data with weak labels. The best performance was obtained using a combination of a pre-processing module to extract information from the tremor spectrum (based on non-negative factorization) and a deep neural network for learning relevant features and detecting tremor segments. We show how the proposed method is able to predict patient self-report measures, and we propose a new metric for monitoring PD tremor (i.e., percentage of tremor over long periods of time), which may be easier to estimate the start and end time points of each tremor event while still providing clinically useful information.

2. ACM CHASE

   Automated tremor detection in Parkinson’s disease using accelerometer signals

   Ada Zhang, Rubén San-Segundo, Stanislav Panev, Griffin Tabor, Katelyn Stebbins, Andrew S. Whitford, Fernando De La Torre, and Jessica K. Hodgins

   In Proceedings of the 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, Washington, DC, 2020

   Abs DOI

   Wearable sensor technology has the potential to transform the treatment of Parkinson’s Disease (PD) by providing objective analysis about the frequency and severity of symptoms in everyday life. However, many challenges remain to developing a system that can robustly distinguish PD motor symptoms from normal motion. Stronger feature sets may help to improve the detection accuracy of such a system. In this work, we explore several feature sets compared across two classification algorithms for PD tremor detection. We find that features automatically learned by a Convolutional Neural Network (CNN) lead to the best performance, although our handcrafted features are close behind. We also find that CNNs benefit from training on data decomposed into tremor and activity spectra as opposed to raw data.

2019

1. ICCV

   

   Person-in-WiFi: Fine-Grained Person Perception Using WiFi

   Fei Wang, Sanping Zhou, Stanislav Panev, Jinsong Han, and Dong Huang

   In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2019

   Bib

   @inproceedings{Wang_2019_ICCV,
     author = {Wang, Fei and Zhou, Sanping and Panev, Stanislav and Han, Jinsong and Huang, Dong},
     title = {Person-in-WiFi: Fine-Grained Person Perception Using WiFi},
     booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
     month = oct,
     year = {2019},
   }

2. T-ITS

   Road Curb Detection and Localization With Monocular Forward-View Vehicle Camera

   Stanislav Panev, Francisco Vicente, Fernando De La Torre, and Véronique Prinet

   IEEE Transactions on Intelligent Transportation Systems, 2019

   DOI

2017

1. EMBC

   Weakly-supervised learning for Parkinson’s Disease tremor detection

   Ada Zhang, Alexander Cebulla, Stanislav Panev, Jessica Hodgins, and Fernando De La Torre

   In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017

   DOI