Highly Accurate Gaze Estimation Using a Consumer RGB-D Sensor
Authors: Reza Shoja Ghiass, Ognjen Arandjelovic
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using EYEDIAP, the standard public dataset for the evaluation of gaze estimation algorithms from RGB-D data, we demonstrate that our method greatly outperforms the state of the art. |
| Researcher Affiliation | Academia | Reza Shoja Ghiass and Ognjen Arandjelovi c Universit e Laval, Qu ebec (QC) G1V 0A6, Canada University of St Andrews, St Andrews KY16 9SX, United Kingdom |
| Pseudocode | No | The paper describes its algorithms using mathematical equations and textual explanations, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about making its source code publicly available or provide a link to a code repository. |
| Open Datasets | Yes | For the evaluation of the proposed method and its comparison with the state of the art we adopted the well known EYEDIAP database [Funes Mora and Odobez, 2012] 1. It is a freely and publicly available standard benchmark for the evaluation of algorithms for gaze estimation from RGB-D data. 1The database can be downloaded from http://www.idiap.ch/ dataset/eyediap. |
| Dataset Splits | No | The paper mentions using a 'training set' and refers to testing on 'subsets' of the EYEDIAP database, but does not provide specific percentages or counts for training, validation, or test splits. |
| Hardware Specification | No | The paper mentions using 'Microsoft Kinect' as a sensor, but does not specify the computational hardware (e.g., GPU, CPU models, memory) used for running the experiments or training the models. |
| Software Dependencies | No | The paper discusses the use of k-nearest neighbour (k NN) regression and adaptive linear regression (ALR) but does not provide specific software names with version numbers for any libraries or tools used. |
| Experiment Setup | Yes | To reduce the dimensionality of the representation we downsample the patches to the uniform scale of 3 5 pixels thus obtaining 15D feature vectors. We used dt = 0.01 which corresponds to the physical distance of 0.01 m. As per (15) we used the Euclidean distance... Following this work we used a sparse training set, as illustrated in Figure 2. |