Smart object counter
Imagine a world where object counting didn’t exist. There would be no quick way to estimate how many people are in a crowded train station, how many cars are passing through an intersection, or even how many products are left on a store shelf. We’d be stuck relying on slow, manual counts—an ineffici...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/181135 |
| _version_ | 1826126078132879360 |
|---|---|
| author | Xiao, Lingyi |
| author2 | Loke Yuan Ren |
| author_facet | Loke Yuan Ren Xiao, Lingyi |
| author_sort | Xiao, Lingyi |
| collection | NTU |
| description | Imagine a world where object counting didn’t exist. There would be no quick way to estimate how many people are in a crowded train station, how many cars are passing through an intersection, or even how many products are left on a store shelf. We’d be stuck relying on slow, manual counts—an inefficient task for humans and impossible at large scales.
In recent years, object counting methods have become more innovative, particularly to overcome the limitations of data scarcity. There are now three main types of objects counting: Few-Shot Counting, Reference-less Counting, and Text-Guided Counting. After evaluation different types of objects counting. It is observed that existing few-shot counting models struggle to generalize across diverse object classes, particularly in complex scenes with varying object sizes and densities. Therefore, this project proposes an improved few-shot counting model that incorporates multi-scale feature fusion techniques with detect and verify paradigm.
Our model introduces a multi-scale feature extraction structure that improves the detection and verification processes, achieving greater adaptability to diverse object appearances, improving counting accuracy, scalability, and generalization across complex visual contexts.
|
| first_indexed | 2025-03-09T14:12:19Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/181135 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T14:12:19Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1811352024-11-19T01:18:22Z Smart object counter Xiao, Lingyi Loke Yuan Ren College of Computing and Data Science yrloke@ntu.edu.sg Computer and Information Science Object counting Computer vision Imagine a world where object counting didn’t exist. There would be no quick way to estimate how many people are in a crowded train station, how many cars are passing through an intersection, or even how many products are left on a store shelf. We’d be stuck relying on slow, manual counts—an inefficient task for humans and impossible at large scales. In recent years, object counting methods have become more innovative, particularly to overcome the limitations of data scarcity. There are now three main types of objects counting: Few-Shot Counting, Reference-less Counting, and Text-Guided Counting. After evaluation different types of objects counting. It is observed that existing few-shot counting models struggle to generalize across diverse object classes, particularly in complex scenes with varying object sizes and densities. Therefore, this project proposes an improved few-shot counting model that incorporates multi-scale feature fusion techniques with detect and verify paradigm. Our model introduces a multi-scale feature extraction structure that improves the detection and verification processes, achieving greater adaptability to diverse object appearances, improving counting accuracy, scalability, and generalization across complex visual contexts. Bachelor's degree 2024-11-19T01:18:22Z 2024-11-19T01:18:22Z 2024 Final Year Project (FYP) Xiao, L. (2024). Smart object counter. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181135 https://hdl.handle.net/10356/181135 en SCSE23-0568 application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Object counting Computer vision Xiao, Lingyi Smart object counter |
| title | Smart object counter |
| title_full | Smart object counter |
| title_fullStr | Smart object counter |
| title_full_unstemmed | Smart object counter |
| title_short | Smart object counter |
| title_sort | smart object counter |
| topic | Computer and Information Science Object counting Computer vision |
| url | https://hdl.handle.net/10356/181135 |
| work_keys_str_mv | AT xiaolingyi smartobjectcounter |