Abstract
This paper presents a solution of the appearance-based people re-identification problem in a surveillance system including multiple cameras with different fields of vision. We first utilize different color-based features, combined with several illuminant invariant normalizations in order to characterize the silhouettes in static frames. A graph-based approach which is capable of learning the global structure of the manifold and preserving the properties of the original data in a lower dimensional representation is then introduced to reduce the effective working space and to realize the comparison of the video sequences. The global system was tested on a real data set collected by two cameras installed on board a train. The experimental results show that the combination of color-based features, invariant normalization procedures and the graph-based approach leads to very satisfactory results.
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Keywords
- Dimensionality Reduction
- Video Sequence
- Multiple Camera
- Invariant Normalization
- Nonlinear Dimensionality Reduction
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Truong Cong, D.N., Achard, C., Khoudour, L., Douadi, L. (2009). Video Sequences Association for People Re-identification across Multiple Non-overlapping Cameras. In: Foggia, P., Sansone, C., Vento, M. (eds) Image Analysis and Processing – ICIAP 2009. ICIAP 2009. Lecture Notes in Computer Science, vol 5716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04146-4_21
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DOI: https://doi.org/10.1007/978-3-642-04146-4_21
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