Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Pedestrian Detection in Crowded Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
On the Removal of Shadows from Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shadow Removal from a Single Image
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Vehicle and Person Tracking in Aerial Videos
Multimodal Technologies for Perception of Humans
Multimodal Technologies for Perception of Humans
Improving object classification in far-field video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A scheme for the detection and tracking of people tuned for aerial image sequences
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
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In this paper, we propose a method for detecting humans in imagery taken from a UAV. This is a challenging problem due to small number of pixels on target, which makes it more difficult to distinguish people from background clutter, and results in much larger searchspace. We propose a method for human detection based on a number of geometric constraints obtained from the metadata. Specifically, we obtain the orientation of groundplane normal, the orientation of shadows cast by humans in the scene, and the relationship between human heights and the size of their corresponding shadows. In cases when metadata is not available we propose a method for automatically estimating shadow orientation from image data. We utilize the above information in a geometry based shadow, and human blob detector, which provides an initial estimation for locations of humans in the scene. These candidate locations are then classified as either human or clutter using a combination of wavelet features, and a Support Vector Machine. Our method works on a single frame, and unlike motion detection based methods, it bypasses the global motion compensation process, and allows for detection of stationary and slow moving humans, while avoiding the search across the entire image, which makes it more accurate and very fast. We show impressive results on sequences from the VIVID dataset and our own data, and provide comparative analysis.