PaperReading

A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification

July 2019

tl;dr: Traffic light dataset with still images as training and video as tracking. No traffic light association is annotated.

Train with still images, and test is on video with tracking.

This is quite different with turn signal light detection.

Turn signal lights (TSL) flicker at much higher rate (1-2 Hz) and have strong temporal features. Traffic lights are most static, with occasional change.
TSL is only on for brief period of time, but traffic lights are on all the time.
TSL has a lot more visual variety than traffic lights.
However, traffic lights is more safety critical and requires higher recall/precision than TSL. It is also hard to associate traffic lights with the ego lane and ego vehicle.

A tracker is used to boost the performance of detection, due to occlusion or off-state due to camera sampling rate. It has a motion model that uses IMU (not available in dataset). It is essentially doing prediction and tracking at the same time.
Each bounding box is trained with predicted confidence supervised by IoU.
The network always predict a confidence score supervised by

Major FP: lamps, decorations, reflections, taillights.
1280x720 images, training annotated at 2 fps, test annotated at 15 fps.
Many traffic lights appear to be off due to difference in camera sampling freq and traffic light refresh rate.
Only 3 448x448 patches are used for inference.
Crop context out for classification –> this will be taken care of directly from the feature map?
Four categories: “green”, “yellow”, “red” and “off”