Similar Object Detection and Tracking in H.264 Compressed Video Using Modified Local Self Similarity Descriptor and Particle Filtering
Authors: Srinivasan Kuppuswamy, Balamurugan Panchanathan
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Object tracking is a dynamic optimization process based on the temporal information related to the previous frames. Proposing a method with higher precision in complex environments is a challenge for researchers in the field of study. In this paper, we have proposed a novel framework for similar object tracking. In our proposed technique we are considering both PETS and Cricket video for input video sequence. The primary steps of suggested technique are preprocessing, background subtraction and segmentation, similar object detection and object tracking. In the preprocessing stage, the adaptive median filter is used to remove the noise from each frame. Next, the foreground and background images are separated and then segmentation of object is carried out by using morphological operation. For similar object detection, the recommended technique uses the modified local self-similarity descriptor and similar object tracking is done by a particle filter. The performance of the suggested technique is evaluated by means of precision, recall, F-measure, FPR, FNR, PWC, FAR, similarity, specificity, and accuracy. An experimental result shows that the proposed technique attains the maximum tracking efficiency for both videos when compared to the existing techniques.