# GazeCom labels

Here we provide labels for the GazeCom data set. The original gaze files and
movie clip are available
[here](http://webmail.inb.uni-luebeck.de/inb-toolsdemos/EyeMovementDataSet.html).

The ARFF representations of the original GazeCom eye tracking files are provided
in the 
[gaze\_arff](https://web.gin.g-node.org/ioannis.agtzidis/gazecom_annotations/src/master/gaze_arff)
folder and are organized per movie clip in the corresponding file.

The hand annotated fixations, saccades, smooth pursuit, and noise (tracking loss,
blinks, physically impossible eye movements) are provided in the 
[ground\_truth](https://web.gin.g-node.org/ioannis.agtzidis/gazecom_annotations/src/master/ground_truth)
folder.

We also provide the annotations from our algorithm as detected from the
[sp\_tool](https://github.com/MikhailStartsev/sp_tool), which is based on
Agtzidis et al. (2016), along with annotations from Berg et al. (2009) and Larsson et al.
(2015) (our re-implementation of the latter is available on the http://michaeldorr.de/smoothpursuit/ page, or via this [link](http://michaeldorr.de/smoothpursuit/larsson_reimplementation.zip)) in the corresponding folders starting with *output_*.

Finally, a collection of 45 hand annotated targets is provided in the 
[targets\_arff](https://web.gin.g-node.org/ioannis.agtzidis/gazecom_annotations/src/master/targets_arff)
folder.

# sp_tool

The implementation of our clustering-based detection algorithm together with a wide variety of
evaluation metrics for eye movement classification can be found 
[here](https://github.com/MikhailStartsev/sp_tool).

# Model performance comparison
A tentative evaluation table of sample-level model evaluation statistics for eye movement classification on GazeCom is available at http://michaeldorr.de/smoothpursuit/.

For a more thorough evaluation (all eye movement classes; including event-level metrics), please refer to our later papers:
* [1D CNN with BLSTM for automated classification of fixations, saccades, and smooth pursuits](https://link.springer.com/article/10.3758/s13428-018-1144-2) (Startsev et al., 2019, Behavior Research Methods).
* [A novel gaze event detection metric that is not fooled by gaze-independent baselines](https://dl.acm.org/citation.cfm?id=3319836) (Startsev et al., 2019, ETRA)

## References

Agtzidis, I., Startsev, M., & Dorr, M. (2016). Smooth pursuit detection
based on multiple observers. In Proceedings of the ninth biennial acm symposium
on eye tracking research & applications (pp. 303-306). ACM.

Berg, D. J., Boehnke, S. E., Marino, R. A., Munoz, D. P., & Itti, L. (2009).
Free viewing of dynamic stimuli by humans and monkeys. Journal of vision, 9(5),
19-19.

Larsson, L., Nyström, M., Andersson, R., & Stridh, M. (2015). Detection of
fixations and smooth pursuit movements in high-speed eye-tracking data.
Biomedical Signal Processing and Control, 18, 145-152.