Data Source:
Team Member:
- Bong Cen Choi
- Gabriel Dejan P.
- Joshua Kaven K.
The database is collected in San Diego, California, USA. The database provides four day-time and two night-time sequences primarily used for testing, providing 23 minutes and 25 seconds of driving in Pacific Beach and La Jolla, San Diego. The stereo image pairs are acquired using the Point Grey’s Bumblebee XB3 (BBX3-13S2C-60) which contains three lenses which capture images with a resolution of 1280 x 960, each with a Field of View(FoV) of 66°. Where the left camera view is used for all the test sequences and training clips. The training clips consists of 13 daytime clips and 5 nighttime clips.
The annotation.zip contains are two types of annotation present for each sequence and clip. The first annotation type contains information of the entire TL area and what state the TL is in. This annotation file is called frameAnnotationsBOX, and is generated from the second annotation file by enlarging all annotation larger than 4x4. The second one is annotation marking only the area of the traffic light which is lit and what state it is in. This second annotation file is called frameAnnotationsBULB.
The annotations are stored as 1 annotation per line with the addition of information such as class tag and file path to individual image files. With this structure the annotations are stored in a csv file, where the structure is exemplified in below listing:
Filename;Annotation tag;Upper left corner X;Upper left corner Y;Lower right corner X;Lower right corner Y;Origin file;Origin frame number;Origin track;Origin track frame number
Annotation file frameAnnotationsBULB
You'll need the following:
- Orange, tool that help you with the modelling and evaluate
- Python Language
- Open Command Prompt/Orange Command Prompt that have python and go to your directory
cd Frontier-Project- Run .py program
python app.py- Wait python's execution program untill it's given the http site
Something like this:
Running on http://127.0.0.1:8050/
- Open your browser, and insert the http
- Press CTRL+C in your Command Prompt to exit from python
- AdaBoost Model
- Random Forest Model
- kNN Model
Jensen MB, Philipsen MP, Møgelmose A, Moeslund TB, Trivedi MM. Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives. I E E E Transactions on Intelligent Transportation Systems. 2016 Feb 3;17(7):1800-1815. Available from, DOI: 10.1109/TITS.2015.2509509
Philipsen, M. P., Jensen, M. B., Møgelmose, A., Moeslund, T. B., & Trivedi, M. M. (2015, September). Traffic light detection: A learning algorithm and evaluations on challenging dataset. In intelligent transportation systems (ITSC), 2015 IEEE 18th international conference on (pp. 2341-2345). IEEE.