A Study on Traditional and Modern Approaches for Person Recognition
文献类型:学位论文
作者 | Muhammad Rauf1,2![]() |
答辩日期 | 2017-04 |
授予单位 | 中国科学院大学 |
授予地点 | 中国科学院大学 |
导师 | Wang Liang |
关键词 | 近邻描述子 步态检索 卷积神经网络 步态识别 知识转移 |
英文摘要 |
Human recognition and identi cation are desirable yet challenging for many
applications including surveillance, computer vision, and robotics. Many biomet-
rics have been used in human recognition and identi cation applications. Gait
biometrics is used to recognize and identify people from videos. The gait biomet-
rics has its advantage of capabilities for recognizing people from distance. It is a
very hot topic and has attracted lot of attention in recent years as we deployed
hundreds of thousand surveillance cameras. These surveillance cameras can be
used for analyzing data on the edge [1]. To deploy the deep learning based pro-
cess on the smart devices, process need to be small in size and able to use low
computing ability. This ability can be archived with the optimized learning tech-
nique. Despite having a complex structure and limitations, the discrimination
power of the gait make its a useful and unique component for person recognition
at a distance.
The work presented in this thesis aims to nd the new strategy for optimized
machine learning and gait recognition and identi cation systems. The object of
the work can be described as:
Find the new strategy for gait recognition and identi cation process.
Investigate the possible solution for deploying the machine learning tech-
nique on the low processing power devices.
In order to complete our goal we study di erent strategies to optimize machine
iv A Study on Traditional and Modern Approaches for Person Recognition
learning and human identi cation. This involved traditional bag-of-the-word
framework and deep learning techniques. The goals that we achieved can be
described as:
Optimization of encoding methods used in Bag-of-Word framework: In
this work, our goal is to draw a relationship between descriptors, and use
this relationship to optimize the encoding methods. We used neighboring
descriptors relationship during the encoding the words. We implemented
this new optimization technique on di erent encoding methods and tested
them on image classi cation and gait recognition.
Retrieving data from videos on the base of their gait biometric: In order
to address the challenges of large-scale video databases, we introduce new
gait based retrieval. The gait based retrieval is used to extract the data
of target person from the video database. We used deep hashing method
to solve the retrieval problem, which outperforms the traditional methods
that we implemented. On next stage this retrieved data can be used for
further recognition and identi cation tasks.
Reducing the computational cost by knowledge transfer technique: With
the increasing use of smart devices and IP cameras, video analysis can be
performed on the edge. Deep learning models are complex in size and costly
in computation, it is not possible to use these models directly on smart
devices and for edge based analysis. We use knowledge transfer technique
to reduce processing and memory cost. We implemented this technique
to transfer the functional knowledge of previous trained models to small
Chapter 0. Abstract v
models. Then these small models can be deployed on smart devices and for
the analysis of data at the end terminal. |
源URL | [http://ir.ia.ac.cn/handle/173211/15509] ![]() |
专题 | 毕业生_博士学位论文 |
作者单位 | 1.中国科学院大学 2.中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Muhammad Rauf. A Study on Traditional and Modern Approaches for Person Recognition[D]. 中国科学院大学. 中国科学院大学. 2017. |
入库方式: OAI收割
来源:自动化研究所
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