  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<EMAIL_ADDRESS>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.
