Accelerating Vehicle Detection in Low-Altitude Airborne Urban Video
Xianbin Cao, Renjun Lin, Pingkun Yan, Xuelong Li · 2011
The limitation of the existing methods of traffic data collection is that they rely on techniques that are strictly local in nature. The airborne system in unmanned aircrafts provides the advantages of wider view angle and higher mobility. However, detecting vehicles in airborne videos is a challenging task because of the scene complexity and platform movement. Most of the techniques used in stationary platforms cannot perform well in this situation. A new and efficient method based on Bayes model is proposed in this paper. This method can be divided into two stages, attention focus extraction and vehicle classification. Experimental results demonstrated that, compared with other representative algorithms, our method obtained better performance with higher detection rate, lower false positive rate and faster detection speed.