An Auto-Annotation Approach for Object Detection and Depth-Based Distance Estimation in Security and Surveillance Systems
Existing object detection and annotation methods in surveillance systems often suffer from inefficiencies due to manual labeling and a lack of accurate distance estimation, which limits their effectiveness in large-scale environments. These limitations reduce the speed and accuracy required for real...
Saved in:
| Main Authors: | Misbah Bibi, Muhammad Faseeh, Anam Nawaz Khan, Atif Rizwan, Qazi Waqas Khan, Rashid Ahmad, Do-Hyeun Kim |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10876157/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Synthetic Data Generation Approach With Dynamic Camera Poses for Long-Range Object Detection in AI Applications
by: Misbah Bibi, et al.
Published: (2024-01-01) -
Deep learning assisted real-time object recognition and depth estimation for enhancing emergency response in adaptive environment
by: Muhammad Faseeh, et al.
Published: (2024-12-01) -
Pretraining instance segmentation models with bounding box annotations
by: Cathaoir Agnew, et al.
Published: (2024-12-01) -
DRCO: Dense-Label Refinement and Cross Optimization for Semi-Supervised Object Detection
by: Yunlong Qin, et al.
Published: (2025-01-01) -
Leveraging Zero-Shot Detection Mechanisms to Accelerate Image Annotation for Machine Learning in Wild Blueberry (<i>Vaccinium angustifolium</i> Ait.)
by: Connor C. Mullins, et al.
Published: (2024-11-01)