55 research outputs found
A Large-scale Distributed Video Parsing and Evaluation Platform
Visual surveillance systems have become one of the largest data sources of
Big Visual Data in real world. However, existing systems for video analysis
still lack the ability to handle the problems of scalability, expansibility and
error-prone, though great advances have been achieved in a number of visual
recognition tasks and surveillance applications, e.g., pedestrian/vehicle
detection, people/vehicle counting. Moreover, few algorithms explore the
specific values/characteristics in large-scale surveillance videos. To address
these problems in large-scale video analysis, we develop a scalable video
parsing and evaluation platform through combining some advanced techniques for
Big Data processing, including Spark Streaming, Kafka and Hadoop Distributed
Filesystem (HDFS). Also, a Web User Interface is designed in the system, to
collect users' degrees of satisfaction on the recognition tasks so as to
evaluate the performance of the whole system. Furthermore, the highly
extensible platform running on the long-term surveillance videos makes it
possible to develop more intelligent incremental algorithms to enhance the
performance of various visual recognition tasks.Comment: Accepted by Chinese Conference on Intelligent Visual Surveillance
201
A multi-channel soft biometrics framework for seamless border crossings
As the number of passengers at border entry points such as airports and rail stations increases, so does the demand for seamless, secure, and fast biometric technologies for verification purposes. Although fingerprints are currently useful biometric technologies, they are intrusive and slow down the end-to-end verification process, increasing the chances of tampering. Emerging as an alternative technology, soft biometrics have proven successful for non-intrusive and rapid verification. Soft biometrics consists of a large set of features from three different modalities of the human body, including the face, body, and essential & auxiliary attachments. This paper proposes a multi-channel soft biometrics framework that leverages soft biometrics technology over traditional biometrics. The framework encapsulates four distinct components: ApparelNet, which verifies essential and auxiliary attachments; A-Net, which measures anthropometric soft biometrics; OneDetect, which predicts global soft biometrics; and RSFS, which develops a set of highly relevant and supportive soft biometrics for verification. The proposed framework addresses several critical limitations of existing biometrics technologies during the verification process at border entry points, such as intrusive behavior, response time, biometric tampering, and privacy issues. The proposed multi-channel soft biometrics framework has been evaluated using several benchmark datasets in the field, such as Front-view Gait (FVG), Pedestrian Attribute Recognition At Far Distance (PETA), and Multimedia and Vision (MMV) Pedestrian. Using heterogeneous datasets enables the testing of each framework component or channel against numerous constrained and unconstrained scenarios. The outcome of the envisioned multi-channel soft biometrics framework is presented based on distinct outcomes from each channel, but it remains focused on determining a single cumulative verification score for verification at border control. In addition, this multi-channel soft biometrics framework has extended applications in several fields, including crowd surveillance, the fashion industry, and e-learning
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
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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