Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Multi View Image Surveillance and Tracking
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
A Multi-Agent Framework for Visual Surveillance
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Counting People in Crowds with a Real-Time Network of Simple Image Sensors
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
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
Fusion of Multi-View Silhouette Cues Using a Space Occupancy Grid
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Feature-Based Sequence-to-Sequence Matching
International Journal of Computer Vision
Robust People Tracking with Global Trajectory Optimization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Multi-texture modeling of 3D traffic scenes
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
ACM Computing Surveys (CSUR)
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A sparsity constrained inverse problem to locate people in a network of cameras
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Cascade of descriptors to detect and track objects across any network of cameras
Computer Vision and Image Understanding
Model-based compressive sensing
IEEE Transactions on Information Theory
Automated multi-camera planar tracking correspondence modeling
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A sparse signal reconstruction perspective for source localization with sensor arrays
IEEE Transactions on Signal Processing - Part II
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
SCOOP: A Real-Time Sparsity Driven People Localization Algorithm
Journal of Mathematical Imaging and Vision
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This paper addresses the problem of localizing people in low and high density crowds with a network of heterogeneous cameras. The problem is recast as a linear inverse problem. It relies on deducing the discretized occupancy vector of people on the ground, from the noisy binary silhouettes observed as foreground pixels in each camera. This inverse problem is regularized by imposing a sparse occupancy vector, i.e., made of few non-zero elements, while a particular dictionary of silhouettes linearly maps these non-empty grid locations to the multiple silhouettes viewed by the cameras network. The proposed framework is (i) generic to any scene of people, i.e., people are located in low and high density crowds, (ii) scalable to any number of cameras and already working with a single camera, (iii) unconstrained by the scene surface to be monitored, and (iv) versatile with respect to the camera's geometry, e.g., planar or omnidirectional.Qualitative and quantitative results are presented on the APIDIS and the PETS 2009 Benchmark datasets. The proposed algorithm successfully detects people occluding each other given severely degraded extracted features, while outperforming state-of-the-art people localization techniques.