143,172 research outputs found
A Multiple Component Matching Framework for Person Re-Identification
Person re-identification consists in recognizing an individual that has
already been observed over a network of cameras. It is a novel and challenging
research topic in computer vision, for which no reference framework exists yet.
Despite this, previous works share similar representations of human body based
on part decomposition and the implicit concept of multiple instances. Building
on these similarities, we propose a Multiple Component Matching (MCM) framework
for the person re-identification problem, which is inspired by Multiple
Component Learning, a framework recently proposed for object detection. We show
that previous techniques for person re-identification can be considered
particular implementations of our MCM framework. We then present a novel person
re-identification technique as a direct, simple implementation of our
framework, focused in particular on robustness to varying lighting conditions,
and show that it can attain state of the art performances.Comment: Accepted paper, 16th Int. Conf. on Image Analysis and Processing
(ICIAP 2011), Ravenna, Italy, 14/09/201
Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks
Person re-identification is an open and challenging problem in computer
vision. Existing approaches have concentrated on either designing the best
feature representation or learning optimal matching metrics in a static setting
where the number of cameras are fixed in a network. Most approaches have
neglected the dynamic and open world nature of the re-identification problem,
where a new camera may be temporarily inserted into an existing system to get
additional information. To address such a novel and very practical problem, we
propose an unsupervised adaptation scheme for re-identification models in a
dynamic camera network. First, we formulate a domain perceptive
re-identification method based on geodesic flow kernel that can effectively
find the best source camera (already installed) to adapt with a newly
introduced target camera, without requiring a very expensive training phase.
Second, we introduce a transitive inference algorithm for re-identification
that can exploit the information from best source camera to improve the
accuracy across other camera pairs in a network of multiple cameras. Extensive
experiments on four benchmark datasets demonstrate that the proposed approach
significantly outperforms the state-of-the-art unsupervised learning based
alternatives whilst being extremely efficient to compute.Comment: CVPR 2017 Spotligh
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