Abstract
This paper proposes a new descriptor for person re-identification building on the recent advances of Fisher Vectors. Specifically, a simple vector of attributes consisting in the pixel coordinates, its intensity as well as the first and second-order derivatives is computed for each pixel of the image. These local descriptors are turned into Fisher Vectors before being pooled to produce a global representation of the image. The so-obtained Local Descriptors encoded by Fisher Vector (LDFV) have been validated through experiments on two person re-identification benchmarks (VIPeR and ETHZ), achieving state-of-the-art performance on both datasets.
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Ma, B., Su, Y., Jurie, F. (2012). Local Descriptors Encoded by Fisher Vectors for Person Re-identification. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33863-2_41
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DOI: https://doi.org/10.1007/978-3-642-33863-2_41
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