Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Feature Based Methods for Structure and Motion Estimation
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Statistical Background Subtraction for a Mobile Observer
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Distributed localization of networked cameras
Proceedings of the 5th international conference on Information processing in sensor networks
Energy-optimized image communication on resource-constrained sensor platforms
Proceedings of the 6th international conference on Information processing in sensor networks
Object tracking in the presence of occlusions via a camera network
Proceedings of the 6th international conference on Information processing in sensor networks
Real-Time Human Posture Reconstruction in Wireless Smart Camera Networks
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Distributed image search in camera sensor networks
Proceedings of the 6th ACM conference on Embedded network sensor systems
Compressive Sensing for Background Subtraction
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
SCOPES: Smart Cameras Object Position Estimation System
EWSN '09 Proceedings of the 6th European Conference on Wireless Sensor Networks
CMOS compressed imaging by Random Convolution
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
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
Live photo mosaic with a group of wireless image sensors
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Compressive Sensing by Random Convolution
SIAM Journal on Imaging Sciences
Heterogeneous traffic performance comparison for 6LoWPAN enabled low-power transceivers
Proceedings of the 6th Workshop on Hot Topics in Embedded Networked Sensors
Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
In-situ soil moisture sensing: measurement scheduling and estimation using compressive sensing
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Efficient cross-correlation via sparse representation in sensor networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Real-time classification via sparse representation in acoustic sensor networks
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Projection matrix optimisation for compressive sensing based applications in embedded systems
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Towards long-term large-scale visual health monitoring using Cyber Glasses
Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare
Hi-index | 0.00 |
Background subtraction is often the first step of many computer vision applications. For a background subtraction method to be useful in embedded camera networks, it must be both accurate and computationally efficient because of the resource constraints on embedded platforms. This makes many traditional background subtraction algorithms unsuitable for embedded platforms because they use complex statistical models to handle subtle illumination changes. These models make them accurate but the computational requirement of these complex models is often too high for embedded platforms. In this paper, we propose a new background subtraction method which is both accurate and computational efficient. The key idea is to use compressive sensing to reduce the dimensionality of the data while retaining most of the information. By using multiple datasets, we show that the accuracy of our proposed background subtraction method is comparable to that of the traditional background subtraction methods. Moreover, real implementation on an embedded camera platform shows that our proposed method is at least 5 times faster, and consumes significantly less energy and memory resources than the conventional approaches. Finally, we demonstrated the feasibility of the proposed method by the implementation and evaluation of an end-to-end real-time embedded camera network target tracking application.