Abstract
Different from conventional sensor networks, video sensor networks are distinctly characterized by their immense information and directional sensing models. In this paper, we propose an innovative, systematic method for image processing in video sensor networks in order to reduce the workload for individual sensors. Given the severe resource constraints on individual sensor nodes, our approach is to employ the redundancy among sensor nodes by partitioning the sensing task among highly correlated sensors. For an object of interest, each sensor only needs to capture and deliver a fraction of the scene of interests and these partial images can be fused at the sink to reconstruct a composite image. In particular, we detail how the sensing task can be partitioned among the sensors and propose an image fusion algorithms based on epipolar line constraint to fuse the received partial images at the sink. The experimental results show that our approach can achieve satisfactory results and we give detailed discussions on the effects of different system parameters.
This work reported in this paper is supported by the NSFC under Grant 60242002, and the NCET program of MOE, China.
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Tao, D., Ma, H., Liu, Y. (2005). Energy-Efficient Cooperative Image Processing in Video Sensor Networks. In: Ho, YS., Kim, HJ. (eds) Advances in Multimedia Information Processing - PCM 2005. PCM 2005. Lecture Notes in Computer Science, vol 3768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11582267_50
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DOI: https://doi.org/10.1007/11582267_50
Publisher Name: Springer, Berlin, Heidelberg
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