ssaket / visual-search-actr

Generate synthetic eye-tracking data using deep learning and ACT-R cognitive model

Generate synthetic eye-tracking data using deep learning and ACT-R cognitive model

A novel approach of devising visual search eye-tracking data using ACT-R EMMA(Salvucci, 2001). It can generate the fixation durations, object encoding time along with scanpath for all the object present in SALICON and COCO-SEARCH-18 datasets using YOLOv3 object recognition algorithm.

Tasks

Driven by my personal interest

Task 1

Generate Scanpaths for the following datasets with the help of ACT-R's visual search module and compare visual search results of ACT-R EMMA with baseline models

• SALICON: Derive encoding time for all objects present in a natural scene image based on YOLO v3 categories.
• COCO-Search-18: Derive encoding time for target search object based on 18 categories.

Task 2 (not yet done)

Generate Scanpaths for videos

Demo

Setup

• Download the SALICON (LSUN) and Coco-Search-18 datasets, create a new directory 'data' and put them inside.

+-- data
|   +-- coco_search_18
|       +-- ...json
|       +--detected
|       +--images
|       +--simulations
|   +-- salicon
|       +--detected
|       +--images
|       +--simulations
+-- compare.py
+-- concat.py
+-- main.py
+-- multiactrsim.py
+-- yolov3.weights

• Download the yolov3 pre-trained weights from https://pjreddie.com/media/files/yolov3.weights
• Refer to start.ps1 script for windows and start.sh script for linux systems

Workflow

• Object Recognition

  • run main.py, for object recognition. It uses yolov3 with probability threshold: 0.6. For usage, refer to start.sh or start.ps1
  • check the output CSVs based on split under data\[dataset]\detected.
  • concat.py, for concatenating all the files into a single CSV file containing image names, list of objects as CSV rows.

• ACT-R simulation

  • multiactrsim.py - For usages, refer to start.sh or start.ps1. It generates the output (CSVs) in data\[dataset]\simulations.

• Evaluation

  • compare.py - For usages, refer to start.sh or start.ps1. Refer/Edit the code in case of any making any specific comparisions
  • visual_search.ipynb - Jupyter notebook for visualizing results and other comparison operations.

Evaluation metrics

• MultiMatch: used in compare.py. For reference, see https://multimatch.readthedocs.io/en/latest/

• ScanMatch: The matlab script for scanmatch can be found in results\matsrc.mat. For reference, see https://seis.bristol.ac.uk/~psidg/ScanMatch/#Tutorial

  • To run for SALICON, open the matsrc.mat in matlab, go inside the salicon folder. results\salicon and press run.

  • To run for COCO-SEARCH-18, open the matsrc.mat in matlab, go inside the coco-search-18 folder. results\coco-search-18 and press run.

Set the parameters for evaluation in the matsrc.mat using the command ScanMatchInfo = ScanMatch_Struct(); For reference, see https://seis.bristol.ac.uk/~psidg/ScanMatch/#Tutorial

visual-search-actr's Issues

introduce process queues for processing multiple subjects in act-r simulation

interpolation of gaze/fixation points to output smooth curves

create a pipeline process for a single image