Abstract
Video contains a large number of motion information. The video– particularly video with moving camera – is segmented based on the relative motion occurring between moving targets and background. By using fusion ability of pulse coupled neural network (PCNN), the target regions and the background regions are fused respectively. Firstly using PCNN fuses the direction of the optical flow fusing, and extracts moving targets from video especially with moving camera. Meanwhile, using phase spectrums of topological property and color pairs (red/green, blue/yellow) generates attention information. Secondly, our video attention map is obtained by means of linear fusing the above features (direction fusion, phase spectrums and magnitude of velocity), which adds weight for each information channel. Experimental results shows that proposed method has better target tracking ability compared with three other methods– Frequency-tuned salient region detection (FT) [5], visual background extractor (Vibe) [6] and phase spectrum of quaternion Fourier transform (PQFT) [1].
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Acknowledgements
This work was supported in part by National Natural Science Foundation of China under grant 61371148.
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Ni, Q., Wang, J., Gu, X. (2015). Moving Target Tracking Based on Pulse Coupled Neural Network and Optical Flow. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_3
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DOI: https://doi.org/10.1007/978-3-319-26555-1_3
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