Privacy-Enabled Object Tracking in Video Sequences Using Compressive Sensing
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Statistical Methods and Models for Video-Based Tracking, Modeling, and Recognition
Foundations and Trends in Signal Processing
Foreground/background segmentation with learned dictionary
ASMCSS'09 Proceedings of the 3rd International Conference on Applied Mathematics, Simulation, Modelling, Circuits, Systems and Signals
Compressive sensing-based image hashing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Modified compressive sensing for real-time dynamic MR imaging
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Modified-CS: modifying compressive sensing for problems with partially known support
IEEE Transactions on Signal Processing
Joint manifolds for data fusion
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Robust and fast collaborative tracking with two stage sparse optimization
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Efficient highly over-complete sparse coding using a mixture model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Local histogram of figure/ground segmentations for dynamic background subtraction
EURASIP Journal on Advances in Signal Processing
A compressive sensing algorithm for many-core architectures
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Compressive evaluation in human motion tracking
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Robust principal component analysis?
Journal of the ACM (JACM)
Efficient background subtraction for tracking in embedded camera networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Block-Sparse RPCA for consistent foreground detection
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Efficient background subtraction for real-time tracking in embedded camera networks
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
A Simple Compressive Sensing Algorithm for Parallel Many-Core Architectures
Journal of Signal Processing Systems
Block covariance based l1 tracker with a subtle template dictionary
Pattern Recognition
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Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
Towards long-term large-scale visual health monitoring using Cyber Glasses
Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
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Compressive sensing (CS) is an emerging field that provides a framework for image recovery using sub-Nyquist sampling rates. The CS theory shows that a signal can be reconstructed from a small set of random projections, provided that the signal is sparse in some basis, e.g., wavelets. In this paper, we describe a method to directly recover background subtracted images using CS and discuss its applications in some communication constrained multi-camera computer vision problems. We show how to apply the CS theory to recover object silhouettes (binary background subtracted images) when the objects of interest occupy a small portion of the camera view, i.e., when they are sparse in the spatial domain. We cast the background subtraction as a sparse approximation problem and provide different solutions based on convex optimization and total variation. In our method, as opposed to learning the background, we learn and adapt a low dimensional compressed representation of it, which is sufficient to determine spatial innovations; object silhouettes are then estimated directly using the compressive samples without any auxiliary image reconstruction. We also discuss simultaneous appearance recovery of the objects using compressive measurements. In this case, we show that it may be necessary to reconstruct one auxiliary image. To demonstrate the performance of the proposed algorithm, we provide results on data captured using a compressive single-pixel camera. We also illustrate that our approach is suitable for image coding in communication constrained problems by using data captured by multiple conventional cameras to provide 2D tracking and 3D shape reconstruction results with compressive measurements.