Detection of Micromobility Vehicles in Urban Traffic Videos
This addresses the challenge of detecting small and thin objects in urban traffic for applications like traffic monitoring, but it is incremental as it adapts existing methods to a specific domain.
The paper tackled the problem of detecting micromobility vehicles like e-scooters and bikes in urban traffic videos by introducing an adapted detection model that combines single-frame and video object detection, resulting in substantial improvement over state-of-the-art methods on a custom dataset.
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.