Human detection in near-infrared spectrum
Fast growing human detection technology has been widely applied in different industries. There is a potential for using near-infrared spectrum to perform the detection task. This dissertation is to study the performance of existing human detection algorithms working in near-infrared spectru...
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| Eará dahkkit: | |
| Materiálatiipa: | Oahppočájánas |
| Giella: | English |
| Almmustuhtton: |
2015
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| Fáttát: | |
| Liŋkkat: | http://hdl.handle.net/10356/65109 |
| Čoahkkáigeassu: | Fast growing human detection technology has been widely applied in different
industries. There is a potential for using near-infrared spectrum to perform the
detection task. This dissertation is to study the performance of existing human
detection algorithms working in near-infrared spectrum.
The human detection task can be accomplished by these two types of detection
methods: full body detection and face detection. Several well-known algorithms
for detecting full bodies and faces are evaluated based on dataset collected in
daytime and nighttime. In daytime, both images in visible spectrum and
near-infrared spectrum are collected while in nighttime only near-infrared images
are collected. The evaluation of these detection methods involves comparisons of
different methods in different spectrums at different time. The comparison results
show the potential of using near-infrared spectrum to detect humans.
A tool for ground truth annotation is implemented to reduce the workload of the
evaluation process. A novel algorithm for bounding rectangle grouping is also
implemented to support the detection experiments. |
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