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Pedestrian Traffic Light Detector for visually impaired people, that can be used as guidance for determining the current phase of a pedestrian traffic light.

You can watch a full demo videohere orhere. (Shot with a iPhone 6s)

• Detection and recognition of pedestrian traffic lights
• Audiovisual and haptic feedback based on the current traffic light phase (Red, Green)
• Accessibility added for easier usage of the app
• Option to customize feedback and detection parameters

The model used in the application is trained with the ML framework YOLOv2.3062 Images have been used during training given the following results on the validation set (630 images):

Please consider that the model is trained on the Red and Green image channels only. Therefore the camera output is beeing manipulated accordinly before beeing sent further down the proccessing pipeline. You can adjust that via a color matrixhere:

iflet colorMatrixFilter=CIFilter(name:"CIColorMatrix"){letr:CGFloat=1letg:CGFloat=1letb:CGFloat=0leta:CGFloat=1

The dataset has been collected in a joint effort between the Hochschule Augsburg and the University of Tuebingen. Students were able to contributeby sending their pictures of pedestrian traffic lights with the LightsCatcher application. Today the datasets consists of around3600 images and4300 annotations.

• Dataset repository
• LightsCatcher (Android)
• LightsCatcher (iOS)

• Model trained on german traffic lights only
• Using the app at night will get you less accurate results
• Multi lane crossings (3+) will get you less accurate results

• Xcode 8 or higher
• iOS 11 or higher

PR´s and/or contributions to the dataset are always very welcome. If you have any further questions, ideas or enquiries, feel free to get in contact either by opening an issue or emailvalpaet@gmail.com.

• Inspired byhollance´sYOLO-CoreML-MPSNNGraph
• pjreddie and all contributors forYOLO/darknet
• Project team@Hochschule Augsburg
• Dataset contribution@University of Tuebingen