Tracking represents a set  of techniques that, fusing information from various sensors, aims at identifying and predicting  objects'  motion. 


Tracking Moving Objects in Image Sequences

The task of extracting information about objects' motions from image frames gathered on complex unstructured scenes is addressed in our work,  We use  a combination of pattern recognition  and tracking techniques. Performance evaluation is difficult because of the high variability present in real sequences. We focus on a detailed analysis of quality parameters by comparing the results of the algorithms with labelled "ground truth" sequences. 

  • F. A. N. Palmieri, A. Pennaccho, G. Di Gennaro, A. Buonanno,  "On  the Evaluation of Tracking Performance on Real Image Sequences," in preparation. 


Resolution Analysis for Closely-Spaced Objects


We analyze a model of a pixel-based sensor in its capability of resolving objects at sub-pixel accuracy.   

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  • Q. Lu, Y. Bar-Shalom, P. Willett, F. Palmieri, R. Ben-Dov and B. Milgrom, "Measurement Extraction for Two Closely-Spaced Objects using an Imaging Sensor," IEEE Transactions on Aerospace and Electronic Systems; doi: 10.1109/TAES.2019.2895587; Print ISSN: 0018-9251; Electronic ISSN: 1557-9603; 11 February 2019.


Target Tracking Over the Horizon

Tracking targets  located in the far distance suffers from the presence of large number of false alarms. We analyze the perfarmance of a PMHT tracker in such a scenario. 

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  • Andrew Finelli, Yaakov Bar-Shalom, Peter Willett, Francesco A. N. Palmieri, Braham Himed, "Target tracking in over the horizon radar," Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 1101803 (7 May 2019);


Use of Ellipsoidal Regions for Conflict Detection

 When agents, such as vessels or vehicles, move in a given space, they have to keep track of the other agents to avoid collisions. The process may be computationally intensive if large number of agents are present on the scene. Ellipsoidal regions are considered as safety regions as they require a small number of parameters.    

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  • Donald McMenemy, D. Sidoti, F. A. N. Palmieri and K.R. Pattipati, “A Fast and Effcient Conflict Detection Method for Ellipsoidal Safety Regions,” IEEE Transaction on Aerospace and Electronic Systems, TAES-201700149, accepted for publication on Oct 22, 2018, Volume: 55, Issue:4, Page(s): 1933-1944; Issue Date: AUGUST 2019, Print ISSN: 0018-9251, Online ISSN: 1557-9603, DOI: 10.1109/TAES.2018.2879551.


Target Tracking from Multiple Cameras

When multiple cameras look over a scene, they have to use prospective analysis to convert image data to spatial positions. Il this work we fuse information from multiple cameras in a Factor Graph-based tracker.  

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  • F. Castaldo and F. A. N. Palmieri, "Target Tracking using Factor Graphs and Multi-Camera Systems", IEEE Transactions on Aerospace and Electronic Systems (TAES), 2015, Volume: 51, Issue: 3 Pages: 1950 - 1960, DOI: 10.1109/TAES.2015.140087.
  • F. Castaldo and F.A. N. Palmieri, "Image Fusion for Object Tracking Using Factor Graphs," Proceedings of the 2014 IEEE Aerospace Conference, March 1-8, Big Sky, Montana, USA. DOI: 10.1109/AERO.2014.6836225.
  • F. Castaldo and F. A. N. Palmieri, "Application of Factor Graphs to Multi-Camera Fusion for Maritime Tracking," Proceedings of 4th International Workshop on Cognitive Information Processing, CIP2014, May 26-28, 2014, Copenhagen, Denmark. DOI: 10.1109/CIP.2014.6844515.
  • F. Castaldo and F.A.N. Palmieri, "A Multi-Camera Multi-Target Tracker based on Factor Graphs," Proceedings of IEEE International Symposium on Innovations in Intelligent Systems and Applications, INISTA2014, June 23-25, 2014, Alberobello, Italy.DOI: 10.1109/INISTA.2014.6873609.


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  • F. Castaldo and F. A. N. Palmieri, "Data Fusion Using a Factor Graph for Ship Tracking in Harbour Scenarios," Proceedings of the 23rd Workshop on Neural Nets, WIRN 2013, May 23-24, Vietri sul Mare, Salerno, Italy, pp. 189-196, Springer, ISBN 978-3-319-04128-5, DOI 10.1007/978-3-319-04129-2.
  • F. A. N. Palmieri, F. Castaldo and G. Marino, ''Harbour Surveillance with Cameras Calibrated with AIS Data,'' Proceedings of the 2013 IEEE Aerospace Conference, Big Sky, Montana, March 2-9, 2013, pp. 1-8, ISBN: 978-1-4673-1812-9, DOI: 10.1109/AERO.2013.6496907.


Sensor Networks for Homeland Security

Efficient fusion of  information from varios sensor modalities is the current challenge in most monitoring systems. We address the issue for various scenarios.  

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  • Buonanno A., M. D'Urso, G. Prisco, M. Felaco, E. F. Meliadò, M. Mattei, F. Palmieri and D. Ciuonzo, ''Mobile Sensor Networks Based on Autonomous Platforms for Homeland Security,'' 2012 IEEE Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS 2012), Naples, IT. Sep. 12-14, 2012, pp 80-84; ISBN: 978-1-4673-2443-4; DOI: : 10.1109/TyWRRS.2012.6381108.
  • D. Ciuonzo, A. Buonanno, Michele D’Urso and F. A.N. Palmieri, "Distributed Classification of Multiple Moving Targets with Binary Wireless Sensor Networks,'' 14th International Conference on Information Fusion, Chicago, IL USA, July 5-8 2011, ISBN: 978-1-4577-0267-9.


Measurement Fusion


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  • Y. Ruan, P. Willett, A. Marrs, F. Palmieri and S. Marano, “Practical Fusion of Quantized Measurements via Particle Filtering,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 44, No. 1, pp. 15-29, January 2008; ISSN : 0018-9251; DOI: 10.1109/TAES.2008.4516986. 
  • Palmieri F., S. Marano, P. Willett, "Measurement Fusion for Target Tracking Under Bandwidth Constraints," Proceedings of 2001 IEEE Aerospace Conference, Volume: 5 , 10-17 March 2001, pp 2179 -2190.