53 research outputs found
Divide and Fuse: A Re-ranking Approach for Person Re-identification
As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type of pedestrian feature is available. In this paper,
we propose a "Divide and use" re-ranking framework for person re-ID. It
exploits the diversity from different parts of a high-dimensional feature
vector for fusion-based re-ranking, while no other features are accessible.
Specifically, given an image, the extracted feature is divided into
sub-features. Then the contextual information of each sub-feature is
iteratively encoded into a new feature. Finally, the new features from the same
image are fused into one vector for re-ranking. Experimental results on two
person re-ID benchmarks demonstrate the effectiveness of the proposed
framework. Especially, our method outperforms the state-of-the-art on the
Market-1501 dataset.Comment: Accepted by BMVC201
An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification
In recent years, a variety of proposed methods based on deep convolutional
neural networks (CNNs) have improved the state of the art for large-scale
person re-identification (ReID). While a large number of optimizations and
network improvements have been proposed, there has been relatively little
evaluation of the influence of training data and baseline network architecture.
In particular, it is usually assumed either that networks are trained on
labeled data from the deployment location (scene-dependent), or else adapted
with unlabeled data, both of which complicate system deployment. In this paper,
we investigate the feasibility of achieving scene-independent person ReID by
forming a large composite dataset for training. We present an in-depth
comparison of several CNN baseline architectures for both scene-dependent and
scene-independent ReID, across a range of training dataset sizes. We show that
scene-independent ReID can produce leading-edge results, competitive with
unsupervised domain adaption techniques. Finally, we introduce a new dataset
for comparing within-camera and across-camera person ReID.Comment: To be published in 2018 15th Conference on Computer and Robot Vision
(CRV
A Discriminatively Learned CNN Embedding for Person Re-identification
We revisit two popular convolutional neural networks (CNN) in person
re-identification (re-ID), i.e, verification and classification models. The two
models have their respective advantages and limitations due to different loss
functions. In this paper, we shed light on how to combine the two models to
learn more discriminative pedestrian descriptors. Specifically, we propose a
new siamese network that simultaneously computes identification loss and
verification loss. Given a pair of training images, the network predicts the
identities of the two images and whether they belong to the same identity. Our
network learns a discriminative embedding and a similarity measurement at the
same time, thus making full usage of the annotations. Albeit simple, the
learned embedding improves the state-of-the-art performance on two public
person re-ID benchmarks. Further, we show our architecture can also be applied
in image retrieval
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