5,489 research outputs found
Learning a Discriminative Null Space for Person Re-identification
Most existing person re-identification (re-id) methods focus on learning the
optimal distance metrics across camera views. Typically a person's appearance
is represented using features of thousands of dimensions, whilst only hundreds
of training samples are available due to the difficulties in collecting matched
training images. With the number of training samples much smaller than the
feature dimension, the existing methods thus face the classic small sample size
(SSS) problem and have to resort to dimensionality reduction techniques and/or
matrix regularisation, which lead to loss of discriminative power. In this
work, we propose to overcome the SSS problem in re-id distance metric learning
by matching people in a discriminative null space of the training data. In this
null space, images of the same person are collapsed into a single point thus
minimising the within-class scatter to the extreme and maximising the relative
between-class separation simultaneously. Importantly, it has a fixed dimension,
a closed-form solution and is very efficient to compute. Extensive experiments
carried out on five person re-identification benchmarks including VIPeR,
PRID2011, CUHK01, CUHK03 and Market1501 show that such a simple approach beats
the state-of-the-art alternatives, often by a big margin.Comment: accepted by CVPR201
Highly Efficient Regression for Scalable Person Re-Identification
Existing person re-identification models are poor for scaling up to large
data required in real-world applications due to: (1) Complexity: They employ
complex models for optimal performance resulting in high computational cost for
training at a large scale; (2) Inadaptability: Once trained, they are
unsuitable for incremental update to incorporate any new data available. This
work proposes a truly scalable solution to re-id by addressing both problems.
Specifically, a Highly Efficient Regression (HER) model is formulated by
embedding the Fisher's criterion to a ridge regression model for very fast
re-id model learning with scalable memory/storage usage. Importantly, this new
HER model supports faster than real-time incremental model updates therefore
making real-time active learning feasible in re-id with human-in-the-loop.
Extensive experiments show that such a simple and fast model not only
outperforms notably the state-of-the-art re-id methods, but also is more
scalable to large data with additional benefits to active learning for reducing
human labelling effort in re-id deployment
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