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008			240529t20222022my a|||fs|||| 000 0 eng d
020			_aTHE0009861 (Local) _qHardback
040			_aUMPSA _beng _cUMP _erda
090			_aPSM .T43 2022 r Bc
100	1		_aThachayane Radhakrishnan, _eauthor.
245	1	0	_aComparative study of person re-identification using deep learning approaches / _cThachayane Radhakrishnan
264		1	_aKuantan, Pahang : _bUMPSA, _c2022
264		4	_c©2022
300			_axi, 135 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aCentre for Mathematical Sciences
502			_aBachelor of Applied Science in Data Analytics with Honours-- Universiti Malaysia Pahang – 2022
504			_aIncludes bibliographical references
520	3		_aPerson Re-Identification is important in video tracking applications and is still a work in progress to date. Person Re-ID is useful in real-time criminal investigation to re-identify a criminal or accused person to overcome security measures. In this work, the concentration is on Image-to-Video Person Re-ID, which relates a certain probe image to videos in the gallery. It has important applications in tracing the position of a lost individual or criminal tracking in real-time despite all the limitations such as low resolution, occlusion, angle and pose variance, illumination change, and heterogeneous matching. There are more than 100 of methods are in use today for Person Re-ID. In this project, a few deep learning approaches will be studied and compared known as Reciprocal Attention Discriminator (READ), Attention loss paired with OSM loss and CL Centers and Temporal Knowledge Propagation (TKP). These methods will be applied to the Person Re-ID setting. The effectiveness of the proposed framework was analysed using state-of-the-art Person Re-ID approaches as mentioned above on the Motian Analysis and Re-Identification Set (MARS), a benchmark dataset. The results were visualized by using Tableau software. From the results, Attention loss paired with OSM loss and CL Centers achieved high mean Average Precision (mAP) which is 82.9% compared to READ and TKP. This study contributes significantly to the wide application of re-identification systems in realistic real-life scenarios.
610	2	0	_aCentre for Mathematical Sciences _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aFinal Year Project
942			_2lcc _cPSM