Enhance multi-object tracking with learnable re-identification
Multi-Object Tracking (MOT) has a broad range of applications in various domains, including video surveillance, autonomous driving, and healthcare monitoring. Despite advancements in MOT algorithms, challenges persist, especially in handling identity switches caused by occlusions and other factors....
Saved in:
Main Author: | Hu, Zihao |
---|---|
Other Authors: | Lin Zhiping |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/176709 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
LiDAR-based 3D object detection and tracking for autonomous driving
by: Luo, Zhipeng
Published: (2024) -
Dynamic index tracking via multi-objective evolutionary algorithm
by: Chiam, S.C., et al.
Published: (2014) -
Model uncertainty guides visual object tracking
by: ZHOU, Lijun, et al.
Published: (2021) -
E-TLD: Event-Based Framework for Dynamic Object Tracking
by: Ramesh, Bharath, et al.
Published: (2022) -
Online multi-face tracking with multi-modality cascaded matching
by: Weng, Zhenyu, et al.
Published: (2024)