PLEASE CHECK THE GITHUB FOR THE LATEST VERSION:

Paper

REQUIREMENT:

• opencv 3.3.0
• tensorflow 1.2.1
• tensorflow slim
• python 3.6

HOW TO USE IT

1. set up opencv dir in ./detector/CMakeLists.txt:

SET("OpenCV_DIR" "<path to the /opencv/build/>")

2. download the LPW dataset and decompress into ./LPW

3. generate videofile.txt

python gen_videofiles.py

4. compile detector:

cd detector
make

5. run the detector

./detector/PupilDetection

6. wait until PupilDetection finish

7. download pretrained model and decompress into ./pretrain/

8. run evaluator

python evaluator/evaluator.py

9. check the result in ./result

Explaination of the result

Structure:

- result
    - <alpha><Is_average_filter><videonumber> (it's the result of each video, so there are 64 files like this. e.g.  0.005False60)
    - <alpha><Is_average_filter><finish time stamp>: e.g. 0.005False60time.struct_time(tm_year=2017, tm_mon=8, tm_mday=22, tm_hour=16, tm_min=12, tm_sec=26, tm_wday=1, tm_yday=234, tm_isdst=1) (it's the result of all each video)

For the result of each video:

line 1: (e.g. ./LPW/23/2.avi) is the file path of the video
line 2: (e.g. NEEDTOIMPROVE11) represent the NumberOfFrame(upperbound)-NumberOfFrame(evaluator). It's not relevant to the paper.
line 3 - line 503: (e.g. Pixcel  242: 0.999) means the accuracy(0.999) with condition that distance(point(predict),point(groundtruth))

For the result of all videos:

line 1: (e.g. NEEDTOIMPROVE11) represent the NumberOfFrame(upperbound)-NumberOfFrame(evaluator). It's not relevant to the paper.
line 2 - line 502: (e.g. Pixcel  242: 0.999) means the accuracy(0.999) with condition that distance(point(predict),point(groundtruth))

Optional: fine-tune

1. make the dataset

python ./train_evaluator/makedataset.py

2. train

download pretrain vgg model and put it into /train_evaluator/pretrain_vgg
python ./train_evaluator/train.py

Code reference

• ./detector/algo.h ./detector/blob_gen.h ./detector/canny_impl.h ./detector/filter_edges.h ./detector/find_best_edge.h:We use the first part of ElSe algorithm[1], which is based on morphologic feature as one of the answer candidates.
• ./evaluator/vgg.pyWe use VGG-16[2] architecture.
• ./LPW/ We use part of (about 1/80) [LPW dataset] to fine-tune the network, and estimate the method on this dataset.

[1]:Fuhl, Wolfgang, et al. "Else: Ellipse selection for robust pupil detection in real-world environments." Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications. ACM, 2016.

[2]:Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).