There are many advantages in using UAVs for search and rescue operations. However, detecting people from a UAV remains a challenge: the embedded detector has to be fast enough and viewpoint robust to detect people in a flexible manner from aerial views. In this paper we propose a processing pipeline to 1) reduce the search space using infrared images and to 2) detect people whatever the roll and pitch angles of the UAV's acquisition system. We tested our approach on a multimodal aerial view dataset and showed that it outperforms the Integral Channel Features (ICF) detector in this context. Moreover, this approach allows real-time compatible detection.
Nowadays pedestrian detectors are fast, scale-robust and quite efficient. Embedded within a UAV such a detector would open new possibilities. In this paper the very well known HOG detector is adapted for UAV use and a new kind of training dataset is proposed in order to increase the detector's angular robustness. A more appropriate set of detection windows, together with a new detection pipeline, is proposed in order to reduce the search space and consequently reduce the computation time. Tests conducted using the improved detector show significantly better results on aerial images.
This paper deals with the problem of obstacle detection in traffic applications. The proposed device allows a diver to receive the current road and vehicle environment information. The environment perception is performed through a fast processing of image sequence acquired from a vision system embedded in a vehicle. The approach is based on frame motion analysis. Firstly, the road motion is computed through a fast and robust wavelets analysis. Then, we detect the areas which have a different motion thanks to a bayesian modelization. Results shown in the paper prove that the proposed method permits the detection of any obstacle on a road in various image conditions.
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