Parallel Kalman filter-based multi-human tracking in surveillance video

Yussiff, A.-L. and Yong, S.-P. and Baharudin, B.B. (2014) Parallel Kalman filter-based multi-human tracking in surveillance video. In: UNSPECIFIED.

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A novel approach to robust and flexible person tracking using an algorithm that integrates state of the arts techniques; an Enhanced Person Detector (EPD) and Kalman filtering algorithm. This proposed algorithm employs multiple instances of Kalman Filter with complex assignment constraints using Graphics Processing Unit (GPU-NVDIA CUDA) as a parallel computing environment for tracking multiple persons even in the presence of occlusion. A Kalman filter is a recursive algorithm which predict the state variables and further uses the observed data to correct the predicted value. Data association in different frames are solved using Hungarian technique to link data in previous frame to the current frame. The benefit of this research is an adoption of standard Kalman Filter for multiple target tracking of humans in real time. This can further be used in all applications where human tracking is needed. The parallel implementation has increased the frame processing speed by 20-30 percent over the CPU implementation. © 2014 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Impact Factor: cited By 6
Uncontrolled Keywords: Computer graphics; Computer graphics equipment; Graphics processing unit; Program processors; Security systems; Target tracking, Human Tracking; Kalman filtering algorithms; Multi-human tracking; Multi-person tracking; Multiple target tracking; Parallel implementations; Parallel-computing environment; Standard Kalman filters, Kalman filters
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 25 Mar 2022 09:03
Last Modified: 25 Mar 2022 09:03

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