6 papers
Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance
Ilker Bozcan, Erdal Kayacan
The detection of contextual anomalies is a challenging task for surveillance since an observation can be considered anomalous or normal in a specific environmental context. An unma…
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance
Ilker Bozcan, Erdal Kayacan
Anomaly detection is a key goal of autonomous surveillance systems that should be able to alert unusual observations. In this paper, we propose a holistic anomaly detection system…
AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance
Ilker Bozcan, Erdal Kayacan
Unmanned aerial vehicles (UAVs) with mounted cameras have the advantage of capturing aerial (bird-view) images. The availability of aerial visual data and the recent advances in ob…
COSMO: Contextualized Scene Modeling with Boltzmann Machines
Ilker Bozcan, Sinan Kalkan
Scene modeling is very crucial for robots that need to perceive, reason about and manipulate the objects in their environments. In this paper, we adapt and extend Boltzmann Machine…
What is (missing or wrong) in the scene? A Hybrid Deep Boltzmann Machine For Contextualized Scene Modeling
İlker Bozcan, Yağmur Oymak, İdil Zeynep Alemdar +1
Scene models allow robots to reason about what is in the scene, what else should be in it, and what should not be in it. In this paper, we propose a hybrid Boltzmann Machine (BM) f…
CINet: A Learning Based Approach to Incremental Context Modeling in Robots
Fethiye Irmak Doğan, İlker Bozcan, Mehmet Çelik +1
There have been several attempts at modeling context in robots. However, either these attempts assume a fixed number of contexts or use a rule-based approach to determine when to i…