Multiple object tracking using a neural cost function

Humphreys, James E.; Hunter, Andrew

doi:10.1016/j.imavis.2008.06.002

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…Humphreys et. al [15] has extensively use cost functions based on SOFM to detect, track and classify object trajectories. The paper also demonstrates improved performance, by breaking down the SOFM into three parts.…”

Section: Related Workmentioning

confidence: 99%

Human behavioural analysis with self-organizing map for ambient assisted living

Appiah

Hunter

Lotfi

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. 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 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints.

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Section: Related Workmentioning

confidence: 99%

Human behavioural analysis with self-organizing map for ambient assisted living

Appiah

Hunter

Lotfi

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Self Cite

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“…Humphreys et. al [7] has extensively use cost functions based on SOFM to detect, track and classify object trajectories. The paper also demonstrates improved performance, by breaking down the SOFM into three parts.…”

Section: Related Workmentioning

confidence: 99%

FPGA-Based Anomalous Trajectory Detection Using SOFM

Appiah

Hunter

Kluge

et al. 2009

Lecture Notes in Computer Science

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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.

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