I N recent years, visual surveillance by intelligent cameras has attracted increasing interest from homeland security, law enforcement, and military agencies. The detection of suspicious (dangerous) items is one of the most important applications. These items can be grouped into two main classes, dynamic suspicious behaviors (e.g., a person attempting to attack others) and static dangerous objects (e.g., luggage or bomb abandoned in public places). The scope of this paper falls into the latter category. Specifically, we investigate how to detect non-flat static objects in a scene using a moving camera. Since these objects may have arbitrary shape, color or texture, state-of-the-art category-specific (e.g., face/car/human) object detection technology, which usually learns one or more specific classifiers based on a large set of similar training images, cannot be applied to our scenario. To deal with this detection problem, we propose a simple but effective framework in DIP
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I have included a variety of artifacts in order to reflect the fact that my experience at engineering has not been focused on just one aspect of learning. The artifacts show my development not only in writing and reading but also in speaking, math, group work, and many other areas. Through my experiences at companies in hardware developing., My skills.... @Plan and arrange events and activities @Delegate responsibility.
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