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Networked Computer Vision Code

 

The code includes a platform and algorithms for simulating smart-camera networks as described in

 

J.C. SanMiguel, A. Cavallaro, Networked Computer Vision: the importance of a holistic simulator,
in IEEE Computer, Vol. 50, No. 7, July 2017, pp.35-43

 

Please cite this paper if you use this code. See also this webpage

 

 

Asynchronous tracker for wireless smart-camera networks Code

 

The code performs the batch asynchronous target tracking described in

 

S. Katragadda, A. Cavallaro, A batch asynchronous tracker for wireless smart-camera networks,

in Proc. of IEEE Int. Conference on Advanced Signal and Video based Surveillance (AVSS), Lecce, 29 August - 1 September, 2017

 

Please cite this paper if you use this code.

 

 

A long short-term memory convolutional neural network for first-person vision activity recognition Code

 

The code classifies the activities from first person videos as described in

 

G. Abebe, A. Cavallaro, A long short-term memory convolutional neural network for first-person vision activity recognition,
Proc. of ICCV workshop on Assistive Computer Vision and Robotics (ACVR), Venice, October 28, 2017

 

Please cite this paper if you use this code.

 

 

Inertial-Vision: cross-domain knowledge transfer for wearable sensors Code

 

The code classifies the activities from first person videos as described in

 

G. Abebe, A. Cavallaro, Inertial-Vision: cross-domain knowledge transfer for wearable sensors,
Proc. of ICCV workshop on Assistive Computer Vision and Robotics (ACVR), Venice, October 28, 2017

 

Please cite this paper if you use this code.

 

 

Active visual tracking in multi-agent scenarios Code

 

The code performs the active target tracking described in

 

Y. Wang, A. Cavallaro, Active visual tracking in multi-agent scenarios,
in Proc. of IEEE Int. Conference on Advanced Signal and Video based Surveillance (AVSS), Lecce, 29 August - 1 September, 2017

 

Please cite this paper if you use this code.

 

 

Average consensus-based asynchronous tracking Code

 

The code performs the neighbour consensus-based target tracking described in

 

S. Katragadda, C. Regazzoni, A. Cavallaro, Average consensus-based asynchronous tracking,

in Proc. of IEEE Int. Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, March 5-9, 2017

 

Please cite this paper if you use this code.

 

 

Neighbour consensus for distributed visual tracking Code

 

The code performs the neighbour consensus-based target tracking described in

 

S. Katragadda, A. Cavallaro, Neighbour consensus for distributed visual tracking,

in Proc. of IEEE Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore, April 7-9, 2015

 

Please cite this paper if you use this code.

 

 

The costs of fusion in smart camera networks Code

 

The code implements the algorithms described in

 

S. Katragadda, J. C. SanMiguel, A. Cavallaro, The costs of fusion in smart camera networks,

in Proc. of ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), Venice, November 4-7, 2014

 

Please cite this paper if you use this code.

 

 

Cost-effective features for re-identification in camera networks Code

 

The code performs a cost and performance based feature selection for matching across cameras described in

 

S.F. Tahir, A. Cavallaro, Cost-effective features for re-identification in camera networks
IEEE Transactions on Circuits and Systems for Video Technology, to appear, 2014

 

Please cite this paper if you use this code.

 

 

Consensus protocols for distributed tracking in wireless camera networks Code (.zip, 761KB, Matlab)

 

The code performs the average consensus-based target tracking described in

 

S. Katragadda, J. C. SanMiguel, A. Cavallaro, Consensus protocols for distributed tracking in wireless camera networks,

in Proc. of IEEE Int. Conf. on Information Fusion (FUSION), Salamanca, Spain, 7-10 July 2014

 

Please cite this paper if you use this code.

 

 

 

Action-based multi-camera synchronization Code

 

The code performs the frame-based multi-view synchronization of video streams described in

 

L. Zini, A. Cavallaro, F. Odone, Action-based multi-camera synchronization
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Vol. 3, Issue 2, pp. 165 - 174, June 2013

 

Please cite this paper if you use this code.

 

 

 

A protocol for evaluating video trackers under real-world conditions Code

 

The code measures the performance on tracking results using a set of trials as described in

 

T. Nawaz, A. Cavallaro, A protocol for evaluating video trackers under real-world conditions

IEEE Transactions on Image Processing, Vol. 22, Issue 4, April 2013, pp. 1354-1361

 

Please cite this paper if you use this code.

 

 

 

WiSE-MNet: an experimental environment for Wireless Multimedia Sensor Networks Code

 

The code is a wireless simulation environment for multimedia networks and is described in

 

C. Nastasi, A. Cavallaro, WiSE-MNet: an experimental environment for Wireless Multimedia Sensor Networks
Proc. of Sensor Signal Processing for Defence (SSPD), London, UK, 28-29 September, 2011

 

Please cite this paper if you use this code.

 

 

 

Distance blurring for space-variant image coding Code (.zip, 237KB, Java)

 

The code performs the depth-based blurring described in

 

T. Popkin, A. Cavallaro and D. Hands, Image coding using depth blurring for aesthetically acceptable distortion,
IEEE Trans. Image Processing, Vol. 20, Issue 11, November 2011

 

Please cite this paper if you use this code.

 

 

 

Space-variant Gaussian blurring Code (.zip, 601KB, Java)

 

The code performs the spatial blurring described in

 

T. Popkin, A. Cavallaro and D. Hands, Accurate and efficient method for smoothly space-variant Gaussian blurring,
IEEE Trans. Image Processing, Vol. 19, No. 5, pp. 1362-1370, May 2010

 

Please cite this paper if you use this code.

 

 

 

Landmark localization and registration for 3D faces Software (.zip, 8.94MB, exe)

 

This software registers 3D faces and calculates their differences using the algorithms described in

 

P. Nair and A. Cavallaro, 3D face detection, landmark localization and registration using a Point Distribution Model,
IEEE Trans. on Multimedia, Vol. 11, No. 4, June 2009

 

Please cite this paper if you use this software.

 

 

 

Multi-feature object trajectory clustering Code (.zip, 2.16MB, Matlab)

 

The code performs the clustering procedure described in

 

N. Anjum and A. Cavallaro, Multi-feature object trajectory clustering for video analysis,
IEEE Trans. on Circuits and Systems for Video Technology, Vol. 18, Issue 11, pp. 1555-1564, November 2008

 

Please cite this paper if you use this code.

 

 

Protocol for evaluating trackers (PFT) Code (.zip, 188KB, Matlab)

 

The code reproduces the evaluation protocol described in

 

T. Nawaz and A. Cavallaro, PFT: A protocol for evaluating video trackers,
in Proc. of IEEE Int. Conf. on Image Processing, Brussels, 11-14 September 2011

 

Please cite this paper if you use this code.

 

 

Efficient depth blurring with occlusion handling Code (.zip, 187KB, Java)

 

The code performs the depth-based blurring described in

 

T. Popkin, A. Cavallaro and D. Hands, Efficient depth blurring with occlusion handling,
in Proc. of IEEE Int. Conf. on Image Processing, Brussels, 11-14 September 2011

 

Please cite this paper if you use this code.

 

 

 

Multi-foveation filtering Code (.zip, 1.98MB, Java)

 

The code performs the spatial filtering described in

 

T. Popkin, A. Cavallaro and D. Hands, Multi-foveation filtering,
in Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Taipei, April 2009, pp. 669-672

 

Please cite this paper if you use this code.

 

 

 

 

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