Detecting Moving Shadows: Algorithms and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Moving Cast Shadow Elimination for Robust Vehicle Extraction Based on 2D Joint Vehicle/Shadow Models
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
A Shadow Elimination Method for Vehicle Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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
Moving Cast Shadow Detection from a Gaussian Mixture Shadow Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Shadow Flow: A Recursive Method to Learn Moving Cast Shadows
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'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
ACM Computing Surveys (CSUR)
Vehicle and Person Tracking in Aerial Videos
Multimodal Technologies for Perception of Humans
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving object classification in far-field video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Incremental Mosaicking of Images from Autonomous, Small-Scale UAVs
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
An Improved Multi-Scale Retinex Algorithm for Vehicle Shadow Elimination Based on Variational Kimmel
UIC-ATC '10 Proceedings of the 2010 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing
Fast multi-aspect 2D human detection
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Vehicle Detection Using Partial Least Squares
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semantic classification in aerial imagery by integrating appearance and height information
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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In this paper, we propose a method for detecting humans and vehicles in imagery taken from a UAV. This is a challenging problem due to a limited number of pixels on target, which makes it more difficult to distinguish objects from background clutter, and results in much larger search space. We propose a method for constraining the search based on a number of geometric constraints obtained from the metadata. Specifically, we obtain the orientation of ground plane normal, the orientation of shadows cast by out of plane objects in the scene, and the relationship between object heights and the size of their corresponding shadows. We use the aforementioned information in a geometry-based shadow, and ground-plane normal blob detector, which provides an initial estimation for locations of shadow casting out of plane (SCOOP) objects in the scene. These SCOOP candidate locations are then classified as either human or clutter using a combination of wavelet features and a Support Vector Machine. To detect vehicles, we similarly find potential vehicle candidates by combining SCOOP and inverted-SCOOP candidates and then classify them using wavelet features and SVM. Our method works on a single frame, and unlike motion detection based methods, it bypasses the entire pipeline of registration, motion detection, and tracking. This method allows for detection of stationary and slowly moving humans and vehicles while avoiding the search across the entire image, allowing accurate and fast localization. We show impressive results on sequences from VIVID and CLIF datasets and provide comparative analysis.