655 research outputs found
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
This work addresses the problem of vehicle identification through
non-overlapping cameras. As our main contribution, we introduce a novel dataset
for vehicle identification, called Vehicle-Rear, that contains more than three
hours of high-resolution videos, with accurate information about the make,
model, color and year of nearly 3,000 vehicles, in addition to the position and
identification of their license plates. To explore our dataset we design a
two-stream CNN that simultaneously uses two of the most distinctive and
persistent features available: the vehicle's appearance and its license plate.
This is an attempt to tackle a major problem: false alarms caused by vehicles
with similar designs or by very close license plate identifiers. In the first
network stream, shape similarities are identified by a Siamese CNN that uses a
pair of low-resolution vehicle patches recorded by two different cameras. In
the second stream, we use a CNN for OCR to extract textual information,
confidence scores, and string similarities from a pair of high-resolution
license plate patches. Then, features from both streams are merged by a
sequence of fully connected layers for decision. In our experiments, we
compared the two-stream network against several well-known CNN architectures
using single or multiple vehicle features. The architectures, trained models,
and dataset are publicly available at https://github.com/icarofua/vehicle-rear
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
Improving Person Re-identification by Attribute and Identity Learning
Person re-identification (re-ID) and attribute recognition share a common
target at learning pedestrian descriptions. Their difference consists in the
granularity. Most existing re-ID methods only take identity labels of
pedestrians into consideration. However, we find the attributes, containing
detailed local descriptions, are beneficial in allowing the re-ID model to
learn more discriminative feature representations. In this paper, based on the
complementarity of attribute labels and ID labels, we propose an
attribute-person recognition (APR) network, a multi-task network which learns a
re-ID embedding and at the same time predicts pedestrian attributes. We
manually annotate attribute labels for two large-scale re-ID datasets, and
systematically investigate how person re-ID and attribute recognition benefit
from each other. In addition, we re-weight the attribute predictions
considering the dependencies and correlations among the attributes. The
experimental results on two large-scale re-ID benchmarks demonstrate that by
learning a more discriminative representation, APR achieves competitive re-ID
performance compared with the state-of-the-art methods. We use APR to speed up
the retrieval process by ten times with a minor accuracy drop of 2.92% on
Market-1501. Besides, we also apply APR on the attribute recognition task and
demonstrate improvement over the baselines.Comment: Accepted to Pattern Recognition (PR
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