Simple In-place Data Augmentation for Surveillance Object Detection

Munkh-Erdene Otgonbold, Ganzorig Batnasan, Munkhjargal Gochoo; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 7208-7216

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.