02721ntm a2200337 i 4500 0 0 lcc 0 0 10000 10000 2024-05-29 0 PSM .T43 2022 r Bc T000003179 2024-05-29 00:00:00 2024-05-29 PSM CD13581 100863 100869 MY-KuUP 20251125110900.0 t||||fr|||| 00| 0 ta 240529t20222022my a|||fs|||| 000 0 eng d THE0009861 (Local) Hardback UMPSA eng UMP rda PSM .T43 2022 r Bc Thachayane Radhakrishnan, author. Comparative study of person re-identification using deep learning approaches / Thachayane Radhakrishnan Kuantan, Pahang : UMPSA, 2022 ©2022 xi, 135 pages : illustrations (some color) ; 30 cm. + 1 CD-ROM rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Centre for Mathematical Sciences Bachelor of Applied Science in Data Analytics with Honours-- Universiti Malaysia Pahang – 2022 Includes bibliographical references Person 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. Centre for Mathematical Sciences Dissertations Universities and colleges Dissertations Final Year Project lcc PSM