Constellation: Benchmarking High-Altitude Object Detection for an Urban Intersection

Mehmet Kerem Turkcan, Sanjeev Narasimhan, Chengbo Zang, Gyung Hyun Je, Bo Yu, Mahshid Ghasemi, Javad Ghaderi, Gil Zussman, Zoran Kostic
Columbia University
2023
Paper Code
Dataset Models

We introduce Constellation, a dataset of 13K images suitable for research on detection of objects in dense urban streetscapes observed from high-elevation cameras, collected for a variety of temporal conditions. The dataset addresses the need for curated data to explore problems in small object detection exemplified by the limited pixel footprint of pedestrians observed tens of meters from above. It enables the testing of object detection models for variations in lighting, building shadows, weather, and scene dynamics.

Abstract

We evaluate contemporary object detection architectures on the dataset, observing that state-of-the-art methods have lower performance in detecting small pedestrians compared to vehicles, corresponding to a 10% difference in average precision (AP). Using structurally similar datasets for pretraining the models results in an increase of 1.8% mean AP (mAP). We further find that incorporating domain-specific data augmentations helps improve model performance. Using pseudo-labeled data, obtained from inference outcomes of the best-performing models, improves the performance of the models. Finally, comparing the models trained using the data collected in two different time intervals, we find a performance drift in models due to the changes in intersection conditions over time.

Poster

BibTeX

@inproceedings{Turkcan2024Constellation,
        author = {Turkcan, Mehmet Kerem and Zang, Chengbo and Narasimhan, Sanjeev and Je, Gyung Hyun and Yu, Bo and Ghasemi, Mahshid and Zussman, Gil and Ghaderi, Javad and Kostic, Zoran},
        title = {Constellation: Benchmarking High-Altitude Object Detection for an Urban Intersection},
        booktitle = {In Preparation},
        year = {2024},
        note = {In Preparation},
      }