Simple In-place Data Augmentation for Surveillance Object Detection
This work addresses the challenge of limited labeled samples for traffic monitoring applications, but it is incremental as it builds on existing augmentation methods tailored for stationary cameras.
The paper tackles the problem of improving object detection in traffic monitoring with limited labeled data by proposing a simple in-place data augmentation technique, achieving comparable performance using only 8.5% of the original data and increasing mAP@.5 from 0.4798 to 0.5025 on the FishEye8K dataset.
Motivated by the need to improve model performance in traffic monitoring tasks with limited labeled samples, we propose a straightforward augmentation technique tailored for object detection datasets, specifically designed for stationary camera-based applications. Our approach focuses on placing objects in the same positions as the originals to ensure its effectiveness. By applying in-place augmentation on objects from the same camera input image, we address the challenge of overlapping with original and previously selected objects. Through extensive testing on two traffic monitoring datasets, we illustrate the efficacy of our augmentation strategy in improving model performance, particularly in scenarios with limited labeled samples and imbalanced class distributions. Notably, our method achieves comparable performance to models trained on the entire dataset while utilizing only 8.5 percent of the original data. Moreover, we report significant improvements, with mAP@.5 increasing from 0.4798 to 0.5025, and the mAP@.5:.95 rising from 0.29 to 0.3138 on the FishEye8K dataset. These results highlight the potential of our augmentation approach in enhancing object detection models for traffic monitoring applications.