Appiah, Kofi and Hunter, Andrew and Dickinson, Patrick and Kluge, Tino and Aiken, Philip (2009) FPGA-based anomalous trajectory detection using SOFM. In: Applied Reconfigurable Computing (ARC) Workshop, 16th - 18th March 2009, Karlsruhe Institut of Technology (KIT), Germany.
Full content URL: http://www.arc2009.org/
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A system for automatically classifying the trajectory of a moving object in a scene as usual or suspicious is presented. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a reconfigurable hardware architecture (Field Programmable Gate Array) to cluster trajectories acquired over a period, in order to detect novel ones. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 15\% classification error, showing the robustness of our approach over others in literature and the speed-up over the use of conventional microprocessor as compared to the use of an off-the-shelf FPGA prototyping board.
|Item Type:||Conference or Workshop Item (Paper)|
|Keywords:||FPGA, Trajectory detection, Self Organizing Feature Map, autonomous detector|
|Subjects:||G Mathematical and Computer Sciences > G730 Neural Computing|
H Engineering > H610 Electronic Engineering
G Mathematical and Computer Sciences > G740 Computer Vision
|Divisions:||College of Science > School of Computer Science|
|Deposited By:||INVALID USER|
|Deposited On:||23 Mar 2009 16:12|
|Last Modified:||29 Jul 2013 09:27|
Available Versions of this Item
FPGA-based Anomalous trajectory detection using SOFM. (deposited 04 Feb 2009 12:56)
FPGA-based Anomalous trajectory detection using SOFM. (deposited 12 Feb 2009 09:04)
- FPGA-based anomalous trajectory detection using SOFM. (deposited 23 Mar 2009 16:12) [Currently Displayed]
- FPGA-based Anomalous trajectory detection using SOFM. (deposited 12 Feb 2009 09:04)
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