Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Azimi, Seyed Majid"'
Autor:
Merkle, Nina, Bahmanyar, Reza, Henry, Corentin, Azimi, Seyed Majid, Yuan, Xiangtian, Schopferer, Simon, Gstaiger, Veronika, Auer, Stefan, Schneibel, Anne, Wieland, Marc, Kraft, Thomas
In order to respond effectively in the aftermath of a disaster, emergency services and relief organizations rely on timely and accurate information about the affected areas. Remote sensing has the potential to significantly reduce the time and effort
Externí odkaz:
http://arxiv.org/abs/2308.05074
In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-resolution aerial imagery by intensive evaluation of a number of traditional and Deep Learning based Single- and Multi-Object Tracking methods. We also desc
Externí odkaz:
http://arxiv.org/abs/2010.09689
Understanding the complex urban infrastructure with centimeter-level accuracy is essential for many applications from autonomous driving to mapping, infrastructure monitoring, and urban management. Aerial images provide valuable information over a la
Externí odkaz:
http://arxiv.org/abs/2007.06102
Multi-class vehicle detection from airborne imagery with orientation estimation is an important task in the near and remote vision domains with applications in traffic monitoring and disaster management. In the last decade, we have witnessed signific
Externí odkaz:
http://arxiv.org/abs/2007.06124
Autor:
Kraus, Maximilian, Azimi, Seyed Majid, Ercelik, Emec, Bahmanyar, Reza, Reinartz, Peter, Knoll, Alois
Multi-pedestrian tracking in aerial imagery has several applications such as large-scale event monitoring, disaster management, search-and-rescue missions, and as input into predictive crowd dynamic models. Due to the challenges such as the large num
Externí odkaz:
http://arxiv.org/abs/2006.15457
Autor:
Azimi, Seyed Majid
On-board real-time vehicle detection is of great significance for UAVs and other embedded mobile platforms. We propose a computationally inexpensive detection network for vehicle detection in UAV imagery which we call ShuffleDet. In order to enhance
Externí odkaz:
http://arxiv.org/abs/1811.06318
Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic monitoring and disaster management. The huge variation in object scale, orientation, category, a
Externí odkaz:
http://arxiv.org/abs/1807.02700
The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lane-wise traffic management, and urban planning. Lane mark
Externí odkaz:
http://arxiv.org/abs/1803.06904
Remote sensing is extensively used in cartography. As transportation networks grow and change, extracting roads automatically from satellite images is crucial to keep maps up-to-date. Synthetic Aperture Radar satellites can provide high resolution to
Externí odkaz:
http://arxiv.org/abs/1802.01445
The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural class
Externí odkaz:
http://arxiv.org/abs/1706.06480