4 papers · 1 filter
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…
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…