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Multiple items tracking are a process of providing unique and consistent ownership of objects during video sequencing. This paper introduces a moving tracking sequence of video compressed H.264 / AVC using the ‘Spatio Temporal Markov Random Field model (ST-MRF)’. The proposed method operates on a compressed domain and tracks moving vectors (MVs) and blocks coding modes (BCMs) from a compressed bitstream. The results presented in this paper suggest that the volume of the object detection algorithm reflects the overall performance of the tracking system. Finally, we investigate how the use of visual definitions in the tracking phase of a tracking system affects performance using Deep SORT. The results presented in this paper suggest that the volume of the discovery algorithm reflects the overall performance of the tracking-by detection system.
Keywords:
Object Tracking, ST-MRF, Deep SORT, Block Coding Modes
Cite Article:
"Multiple Object Tracking using STMRF and YOLOv4 Deep SORT in Surveillance Video", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 6, page no.43 - 51, June-2022, Available :http://www.ijrti.org/papers/IJRTI2206008.pdf
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000205287
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator