Detection of Micromobility Vehicles in Urban Traffic Videos
Khalil Sabri Célia Djilali Guillaume-Alexandre Bilodeau Nicolas Saunier Wassim Bouachir
Urban traffic environments present unique challenges for object detection,
particularly with the increasing presence of micromobility vehicles like
e-scooters and bikes. To address this object detection problem, this work
introduces an adapted detection model that combines the accuracy and speed of
single-frame object detection with the richer features offered by video object
detection frameworks. This is done by applying aggregated feature maps from
consecutive frames processed through motion flow to the YOLOX architecture.
This fusion brings a temporal perspective to YOLOX detection abilities,
allowing for a better understanding of urban mobility patterns and
substantially improving detection reliability. Tested on a custom dataset
curated for urban micromobility scenarios, our model showcases substantial
improvement over existing state-of-the-art methods, demonstrating the need to
consider spatio-temporal information for detecting such small and thin objects.
Our approach enhances detection in challenging conditions, including
occlusions, ensuring temporal consistency, and effectively mitigating motion
blur.