Improving Object Detection to Fisheye Cameras with Open-Vocabulary Pseudo-Label Approach

Fish-eye cameras have long been employed in traffic surveillance systems to allow for wider observation of the roads. Despite their widespread use, limited computer vision research is tailored explicitly to images captured by fish-eye cameras. The AI City Challenge 2024 - Track 4 introduces a novel fish-eye camera dataset for the 2D road object detection task. This paper proposes a framework designed to detect objects in fish-eye camera images. Our approach involves several key steps: first, we generate image data to bridge the representation gap between day and night images. Next, we leverage zero-shot open vocabulary detection to produce pseudo-labels, aiding in training supervised object detection models. Additionally, we optimize the model's hyper-parameters and inference configuration for better performance. Finally, we apply various post-processing techniques to enhance detection performance. Our solution achieves a final F1 score of 0.6194 in the AI City Challenge 2024 - Track 4, ranking third among competing teams. The source code is available at GitHub Repo.