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Real-time moving object detection, classification, and tracking capabilities are presented with system operates on both color and gray-scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. Object detection in a video is usually performed by object detectors or background subtraction techniques. The proposed method determines the threshold automatically and dynamically depending on the intensities of the pixels in the current frame. In this method, it updates the background model with learning rate depending on the differences of the pixels in the background model of the previous frame. The graph cut segmentation-based region merging algorithm approaches achieve both segmentation and optical flow computation accurately and they can work in the presence of large camera motion. The algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group, and vehicle.

No abstract

In the software development cycle, the requirement traceability link is one of the important factors. Information retrieval techniques typically produce links with low precision and/or recall because, due to their very nature, they depend on the textual similarity between requirements and source code. The developers may not evolve requirements in synchronization with source code. But, they frequently update other sources of information including CVS/SVN repositories, bug-tracking systems, mailing lists, forums, and blogs. These sources of information will be used to build improved requirement traceability-recovery approaches. After the source code was developed, the developer compiles the source code and commits the source code into versions. Then admin needs to compare the templates and versions based on similarity measurement. Based on textual comparison, the similarity is calculated. The admin can do the same process for the forum requirements.