Object tracking using opencv and python
| dc.contributor.author | Norimtiyazah Binti Mohd Kamarulzaman Shah | |
| dc.date.accessioned | 2021-03-01T02:57:53Z | |
| dc.date.available | 2021-03-01T02:57:53Z | |
| dc.date.issued | 2019-06 | |
| dc.description.abstract | Object tracking is the behavior of the moving objects with adaptive search method. Several are not match, split case and occlusion case respectively. By this problem we can track object using the multiple object tracking by finding algorithms to track the moving object in successive connecting frame. With this method, the extraction of the background subtraction of foreground subtraction and finally provide bounding box while colour modelled for each pixel is done. After that, the resulting tensor will contain magnitude and orientation information that give input to Gaussian component. It will be compared to colour model in connecting frame. If the comparison is successful, it will track the object but if it not similar it will track the other objects. In this research, it emphasizes how the multiple object tracking work across video frame. Therefore, the result for this multiple object isto analyse the selection multiple capability in all tracker algorithm within its speed and the direction of the motion. In this case, the best and suitable tracker for this research is CSRT as it has higher accuracy in object tracking than the other tracker algorithms. However, it has slower speed than KCF but more accurate in tracking the multiple object across video frame. Thus, thisresearch conclude that every tracker has its own advantages and disadvantages. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/11609 | |
| dc.language.iso | en | en_US |
| dc.title | Object tracking using opencv and python | en_US |
| dc.type | Other | en_US |
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