FPGA-Based Anomalous Trajectory Detection Using SOFM

Authors:
Kofi Appiah;Andrew Hunter;Tino Kluge;Philip Aiken;Patrick Dickinson
Affiliations:
Dept. of Computing & Informatics, University of Lincoln, UK;Dept. of Computing & Informatics, University of Lincoln, UK;Statistical Laboratory, University of Cambridge, UK;SecuraCorp, Kirkland, USA;Dept. of Computing & Informatics, University of Lincoln, UK
Venue:
ARC '09 Proceedings of the 5th International Workshop on Reconfigurable Computing: Architectures, Tools and Applications
Year:
2009

Citing 14
Cited 2

Learning the distribution of object trajectories for event recognition

BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Learning Patterns of Activity Using Real-Time Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Novelty Detection in Video Surveillance Using Hierarchical Neural Networks

ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Application of the Self-Organizing Map to Trajectory Classification

VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Image segmentation and pattern matching based FPGA/ASIC implementation architecture of real-time object tracking

ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Visual Recognition of Manual Tasks Using Object Motion Trajectories

AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Embedded active vision system based on an FPGA architecture

EURASIP Journal on Embedded Systems
Multiple object tracking using a neural cost function

Image and Vision Computing
Using Inactivity to Detect Unusual behavior

WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Real time architectures for moving-objects tracking

ARC'07 Proceedings of the 3rd international conference on Reconfigurable computing: architectures, tools and applications
Object Trajectory-Based Activity Classification and Recognition Using Hidden Markov Models

IEEE Transactions on Image Processing
A Statistical Video Content Recognition Method Using Invariant Features on Object Trajectories

IEEE Transactions on Circuits and Systems for Video Technology
Event Detection Using Trajectory Clustering and 4-D Histograms

IEEE Transactions on Circuits and Systems for Video Technology
Trajectory-Based Anomalous Event Detection

IEEE Transactions on Circuits and Systems for Video Technology

Design Assurance Strategy and Toolset for Partially Reconfigurable FPGA Systems

ACM Transactions on Reconfigurable Technology and Systems (TRETS)
An FPGA-based fast classifier with high generalization property

ACM SIGARCH Computer Architecture News

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.