 |
Weakly Supervised Domain Adaptation for Video Surveillance in Large-scale Camera Networks (P C Yuen et al.)
With the growing installation of surveillance video cameras in both private and public areas,
closed-circuit TV has evolved from single-camera to multiple-camera systems, and more recently
to large-scale camera networks. Current surveillance applications such as those employed
in shopping malls and residential buildings consist of multiple-camera networks of up
to hundreds of cameras, and many large cities around the world, including Beijing, London,
New York and Seoul, have installed hundreds of thousands of cameras as part of their surveillance
apparatus. Whilst large-scale camera network hardware is generally well-designed
and well-installed, the development of intelligent video analysis software lags far behind.
Research on such intelligent video surveillance topics as person re-identification and human
activity recognition has achieved great success in the past decade. Most of the algorithms
involved are learning-based and assume that the joint distribution of the training data and
label is similar to that in the testing phase. However, in large-scale camera networks, owing
to different capturing environments, camera poses and other unpredictable conditions, this
assumption is difficult to satisfy. The result is performance degradation, the degree of which
depends on the level of joint distribution mismatch between the training data and testing data.
A preliminary experiment on person re-identification showed that accuracy can be degraded
by 13% in rank-10 accuracy (please refer to Section 2 for details). Therefore, the joint distribution
mismatch problem must be resolved if existing video surveillance methods are to be
deployed in large-scale camera networks.
Domain adaptation has been proved as a promising approach to solving the joint distribution
mismatch problem. Existing algorithms can be roughly classified into three approaches: supervised,
unsupervised and semi-supervised. Whilst different approaches are designed for
different situations, each approach has its own assumptions/requirements, which are difficult
to satisfy in most video surveillance applications in large-scale camera networks. Therefore,
the performance of these algorithms may deteriorate dramatically, or they cannot be employed
directly.
To overcome this problem, this project proposes a weakly supervised domain adaptation approach,
the basic idea of which is to employ the available information in large-scale camera
networks to estimate weakly positive information (the weakly positive information refers to
information about positive data rather than labels of that data). A new theory and algorithm
allowing weakly supervised domain adaptation to learn and improve a classification model
will then be developed on the basis of that estimated information. The theory and algorithm
to be developed will be generic in nature, and applicable to video surveillance applications in
large-scale camera networks
Grant Support:
This project is supported by the Research Grants Council (RGC), Hong Kong SAR, China (Project HKBU12202514).
For further information on this research topic, please contact Prof. P C Yuen.
 |