Movement tracking of cognitive processes: A tutorial using mousetrap

Movement tracking is a novel process tracing method promising unique access to the temporal dynamics of cognitive processes. The method involves high-resolution tracking of the hand or handheld devices, e.g., a computer mouse, while they are used to make a choice. In contrast to other process tracing methods, which mostly focus on information acquisition, movement tracking focuses on the processes of information integration and preference formation. In this article, we present a tutorial to movement tracking of cognitive processes with the mousetrap R package. We will address all steps of the research process from design to interpretation, with a particular focus on data processing and analysis. Using a representative working example, we will demonstrate how the various steps of movement tracking analysis can be implemented with mousetrap and provide thorough explanations on their theoretical background and interpretation. Finally, we present a list of recommendations to assist researchers in addressing their own research question using movement tracking of cognitive processes.

Human Computer Interface using Eye Gazing with error fixation in Smooth and Saccadic Eye Movement

Human Computer Interface (HCI) requires proper coordination and definition of features that serve as input to the system. The parameters of a saccadic and smooth eye movement tracking are observed and a comparison is drawn for HCI. This methodology is further incorporated with Pupil, OpenCV and Microsoft Visual Studio for image processing to identify the position of the pupil and observe the pupil movement direction in real-time. Once the direction is identified, it is possible to determine the accurate cruise position which moves towards the target. To quantify the differences between the step-change tracking of saccadic eye movement and incremental tracking of smooth eye movement, the test was conducted on two users. With the help of incremental tracking of smooth eye movement, an accuracy of 90% is achieved. It is found that the incremental tracking requires an average time of 7.21s while the time for step change tracking is just 2.82s. Based on the observations, it is determined that, when compared to the saccadic eye movement tracking, the smooth eye movement tracking is over four times more accurate. Therefore, the smooth eye tracking was found to be more accurate, precise, reliable, and predictable to use with the mouse cursor than the saccadic eye movement tracking.

Cosmic Geodesy Contribution to Geodynamics Monitoring

The cosmic geodesy provides methods and ways of various data acquisition. The collected data may be used for research, calculations and analysis in different fields of interest. According to the reliability and redundancy of data provided by cosmic geodesy methods, it is possible to contribute to the geodynamics monitoring. The geodynamics monitoring enables the tectonic plates movement tracking and predicts the movements which may result in disasters. Applying data provided by cosmic geodesy methods in the form of permanent observation station positions and their changes in time, in calculations, whose physical nature is based on the continuum mechanics, makes possible to monitor the direction, locality and size of visualised deformation tensors.

Object Detection and Movement Tracking Using Tubelets and Faster RCNN Algorithm with Anchor Generation

Object detection in images and videos has become an important task in computer vision. It has been a challenging task due to misclassification and localization errors. The proposed approach explored the feasibility of automated detection and tracking of elephant intrusion along forest border areas. Due to an alarming increase in crop damages resulted from movements of elephant herds, combined with high risk of elephant extinction due to human activities, this paper looked into an efficient solution through elephant’s tracking. The convolutional neural network with transfer learning is used as the model for object classification and feature extraction. A new tracking system using automated tubelet generation and anchor generation methods in combination with faster RCNN was developed and tested on 5,482 video sequences. Real-time video taken for analysis consisted of heavily occluded objects such as trees and animals. Tubelet generated from each video sequence with intersection over union (IoU) thresholds have been effective in tracking the elephant object movement in the forest areas. The proposed work has been compared with other state-of-the-art techniques, namely, faster RCNN, YOLO v3, and HyperNet. Experimental results on the real-time dataset show that the proposed work achieves an improved performance of 73.9% in detecting and tracking of objects, which outperformed the existing approaches.