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Vikas Reddy
BEng, PhD

Research Fellow
Queensland University of Technology
2 George St. Brisbane 4001

Email:   vikas (döt) reddy (ät) ieee (döt) org
             rvikas2333 (ät) gmail (döt) com   


Current research interests include signal process, machine learning, pattern recognition, computer vision, video compression and Bayesian networks.



Selected Publications


V. Reddy, C. Sanderson, B.C. Lovell.
Improved Foreground Detection via Block-based Classifier Cascade with Probabilistic Decision Integration
IEEE Transactions on Circuits and Systems for Video Technology 23 (1) 2013.

V. Reddy, C.Sanderson, B.C. Lovell
Improved Anomaly Detection in Crowded Scenes via Cell-based Analysis of Foreground Speed, Size and Texture 
MLvMA Workshop, IEEE Conf. Computer Vision and Pattern Recognition (CVPR), USA, 2011.



V. Reddy, C.Sanderson, Andres Sanin, B.C. Lovell
MRF-based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos 
10th Asian Conference on Computer Vision (ACCV), NZ, 2010.

V. Reddy, C.Sanderson, Andres Sanin, B.C. Lovell
Adaptive Patch-Based Background Modelling for Improved Foreground Object Segmentation and Tracking 
7th IEEE International Conference on Advanced Video and Signal-based Surveillance, (AVSS), USA, 2010.
Demo videos: Video_1 Video_2 Video_3 Video_4

V. Reddy, C.Sanderson, B.C. Lovell
Robust Foreground Object Segmentation via Adaptive Region-Based Background Modelling 
20th IEEE International Conference on Pattern Recognition, (ICPR), Turkey, 2010.

V. Reddy, C.Sanderson, B.C. Lovell
An efficient and robust sequential algorithm for background estimation in video surveillance 
16th IEEE International Conference on Image Processing, (ICIP), Egypt, 2009.

V. Reddy, C.Sanderson, B.C. Lovell, A. Bigdeli
An efficient background estimation algorithm for embedded smart cameras 
3rd ACM/IEEE International Conference on Distributed Smart Cameras, (ICDSC), Italy, 2009.

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Source Code

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Demo Videos

Background Estimation/Initialization



Foreground Segmentation

Demo 2    Demo 3    Demo 4    Demo 5

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