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dc.contributor.authorChakrabarty Siddhanta
dc.description.abstractDetecting and tracking objects in videos and images is a rapidly growing field of research. Identifying, recognising, detecting and tracking objects such as humans, cars, obstacles etc. has many applications. There are a large number of methods to perform these tasks. They vary in performance, quality of results, type of results, types of raw data and so on. This project aims to detect and track objects, exclusively from depth video. Depth video is a video sequence captured by a Kinect camera with the pixel index values of each frame being the distance of the real point represented by that pixel from the camera. Detection is performed using a morphological segmentation technique called watershed transform. The detection parameters chosen are derived from the objects of interest in the test dataset. Two methods, edge-based detection and region-based detection, are used for pre-processing and the result with the largest detected area is selected. The two methods complement each other in many cases, making the use of both necessary. The objects of interest in the dataset are human beings. Thus various types of situations have been used to test the efficiency of the algorithm, such as crowded areas, noncrowded areas, single object, multiple objects, occluded objects and non-occluded objects. Challenges arise when multiple objects move across a scene. This problem has been addressed with a method for tracking multiple objects with, theoretically, no limit to the maximum number of objects. However, occlusion deteriorates algorithm performance. Test results are presented and compared to existing methods. While the accuracy and efficiency of the proposed system is moderately high, its implementation is simple, reducing processing time greatly.en_US
dc.format.extent60 p.en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processingen_US
dc.titleDetecting and tracking objects in RGB-D videoen_US
dc.contributor.supervisorChan Kap Luken_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Signal Processing)en_US
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