203,011 research outputs found
Structured learning of metric ensembles with application to person re-identification
Matching individuals across non-overlapping camera networks, known as person
re-identification, is a fundamentally challenging problem due to the large
visual appearance changes caused by variations of viewpoints, lighting, and
occlusion. Approaches in literature can be categoried into two streams: The
first stream is to develop reliable features against realistic conditions by
combining several visual features in a pre-defined way; the second stream is to
learn a metric from training data to ensure strong inter-class differences and
intra-class similarities. However, seeking an optimal combination of visual
features which is generic yet adaptive to different benchmarks is a unsoved
problem, and metric learning models easily get over-fitted due to the scarcity
of training data in person re-identification. In this paper, we propose two
effective structured learning based approaches which explore the adaptive
effects of visual features in recognizing persons in different benchmark data
sets. Our framework is built on the basis of multiple low-level visual features
with an optimal ensemble of their metrics. We formulate two optimization
algorithms, CMCtriplet and CMCstruct, which directly optimize evaluation
measures commonly used in person re-identification, also known as the
Cumulative Matching Characteristic (CMC) curve.Comment: 16 pages. Extended version of "Learning to Rank in Person
Re-Identification With Metric Ensembles", at
http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Paisitkriangkrai_Learning_to_Rank_2015_CVPR_paper.html.
arXiv admin note: text overlap with arXiv:1503.0154
Deep Attributes Driven Multi-Camera Person Re-identification
The visual appearance of a person is easily affected by many factors like
pose variations, viewpoint changes and camera parameter differences. This makes
person Re-Identification (ReID) among multiple cameras a very challenging task.
This work is motivated to learn mid-level human attributes which are robust to
such visual appearance variations. And we propose a semi-supervised attribute
learning framework which progressively boosts the accuracy of attributes only
using a limited number of labeled data. Specifically, this framework involves a
three-stage training. A deep Convolutional Neural Network (dCNN) is first
trained on an independent dataset labeled with attributes. Then it is
fine-tuned on another dataset only labeled with person IDs using our defined
triplet loss. Finally, the updated dCNN predicts attribute labels for the
target dataset, which is combined with the independent dataset for the final
round of fine-tuning. The predicted attributes, namely \emph{deep attributes}
exhibit superior generalization ability across different datasets. By directly
using the deep attributes with simple Cosine distance, we have obtained
surprisingly good accuracy on four person ReID datasets. Experiments also show
that a simple metric learning modular further boosts our method, making it
significantly outperform many recent works.Comment: Person Re-identification; 17 pages; 5 figures; In IEEE ECCV 201
Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification
Online multi-object tracking is a fundamental problem in time-critical video
analysis applications. A major challenge in the popular tracking-by-detection
framework is how to associate unreliable detection results with existing
tracks. In this paper, we propose to handle unreliable detection by collecting
candidates from outputs of both detection and tracking. The intuition behind
generating redundant candidates is that detection and tracks can complement
each other in different scenarios. Detection results of high confidence prevent
tracking drifts in the long term, and predictions of tracks can handle noisy
detection caused by occlusion. In order to apply optimal selection from a
considerable amount of candidates in real-time, we present a novel scoring
function based on a fully convolutional neural network, that shares most
computations on the entire image. Moreover, we adopt a deeply learned
appearance representation, which is trained on large-scale person
re-identification datasets, to improve the identification ability of our
tracker. Extensive experiments show that our tracker achieves real-time and
state-of-the-art performance on a widely used people tracking benchmark.Comment: ICME 201
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