Accelerated hardware video object segmentation: From foreground detection to connected components labelling

Authors:
Kofi Appiah;Andrew Hunter;Patrick Dickinson;Hongying Meng
Affiliations:
Department of Computing and Informatics, University of Lincoln, Brayford Campus, LN6 7TS, United Kingdom;Department of Computing and Informatics, University of Lincoln, Brayford Campus, LN6 7TS, United Kingdom;Department of Computing and Informatics, University of Lincoln, Brayford Campus, LN6 7TS, United Kingdom;Department of Computing and Informatics, University of Lincoln, Brayford Campus, LN6 7TS, United Kingdom
Venue:
Computer Vision and Image Understanding
Year:
2010

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Abstract

This paper demonstrates the use of a single-chip FPGA for the segmentation of moving objects in a video sequence. The system maintains highly accurate background models, and integrates the detection of foreground pixels with the labelling of objects using a connected components algorithm. The background models are based on 24-bit RGB values and 8-bit gray scale intensity values. A multimodal background differencing algorithm is presented, using a single FPGA chip and four blocks of RAM. The real-time connected component labelling algorithm, also designed for FPGA implementation, run-length encodes the output of the background subtraction, and performs connected component analysis on this representation. The run-length encoding, together with other parts of the algorithm, is performed in parallel; sequential operations are minimized as the number of run-lengths are typically less than the number of pixels. The two algorithms are pipelined together for maximum efficiency.