FPGA-based anomalous trajectory detection using SOFM

Appiah, Kofi, Hunter, Andrew, Dickinson, Patrick , 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.

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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.

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
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ID Code:1800
Deposited On:23 Mar 2009 16:12

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