| Summary: | 碩士 === 國立中興大學 === 電機工程學系所 === 105 === Nowadays, automatic visual system with high resolution video stream application is much more common in our life. With huge progress of computer and mobile system, we can use this powerful tool to help us conquer the massive computation of visual analysis and their related applications. Amount the visual system, objects tracking is almost the most basic but complicated subject, the user always wants to find the perfect balance between computation and precision, with more complex application, we used to find more and more new algorithms to solve unexpected problems.
In this architecture, in order to increase the accuracy of multi-object tracking, I use the scale invariant feature transform to establish the ID of each registered objects. After matching, all the features in database with searching area, the major problem is to find the robust pairs of those matching key points. With this key points, I can find the precise transform matrix to locate the update set of key points in searching area. Repeat all this rule to find each relocate objects in the new input frame.
Because of massive computation, I have to speed up a part of my design to catch up the real time implementation requirement. So I decide to build a hardware version of SIFT feature detection to replace the software one, take advantage of high parallelism of the algorithm of detection itself, the hardware can really reduce much of computation to speed up my original architecture.
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