Detecting objects in aerial images is challenging for at least two reasons:(1) target objects
like pedestrians are very small in pixels, making them hardly distinguished from surrounding …

Directly benefiting from the deep learning methods, object detection has witnessed a great
performance boost in recent years. However, drone-view object detection remains …

In this paper, we propose a deep convolutional neural network solution to the analysis of
image data for the detection of rail surface defects. The images are obtained from many …

Detecting small objects such as vehicles in satellite images is a difficult problem. Many
features (such as histogram of oriented gradient, local binary pattern, scale-invariant feature …

Vehicle classification is a hot computer vision topic, with studies ranging from ground-view to
top-view imagery. Top-view images allow understanding city patterns, traffic management …

Deep convolutional Neural Networks (DNN) is the state-of-the-art machine learning method.
It has been used in many recognition tasks including handwritten digits, Chinese words and …

Wide Area Motion Imagery (WAMI) enables the surveillance of tens of square kilometers with
one airborne sensor Each image can contain thousands of moving objects. Applications …

Information Fusion consists of organizing a set of data for correlation in time, association
over multimodal collections, and estimation in space. There exist many methods for object …

Real-time information fusion based on WAMI (Wide-Area Motion Imagery), FMV (Full Motion
Video), and text data is highly desired for many mission critical emergency or military …

Detection of moving vehicles in wide area motion imagery (WAMI) is increasingly important,
with promising applications in surveillance, traffic scene understanding and public service …