L. R. D. Murthy

Murthy L. R. D., Abhishek Mukhopadhyay, Shambhavi Aggarwal et al.

Appearance-based gaze estimation systems have shown great progress recently, yet the performance of these techniques depend on the datasets used for training. Most of the existing gaze estimation datasets setup in interactive settings were recorded in laboratory conditions and those recorded in the wild conditions display limited head pose and illumination variations. Further, we observed little attention so far towards precision evaluations of existing gaze estimation approaches. In this work, we present a large gaze estimation dataset, PARKS-Gaze, with wider head pose and illumination variation and with multiple samples for a single Point of Gaze (PoG). The dataset contains 974 minutes of data from 28 participants with a head pose range of 60 degrees in both yaw and pitch directions. Our within-dataset and cross-dataset evaluations and precision evaluations indicate that the proposed dataset is more challenging and enable models to generalize on unseen participants better than the existing in-the-wild datasets. The project page can be accessed here: https://github.com/lrdmurthy/PARKS-Gaze

LRD Murthy, Abhishek Mukhopadhyay, Varshit Yellheti et al.

Eye gaze controlled interfaces allow us to directly manipulate a graphical user interface just by looking at it. This technology has great potential in military aviation, in particular, operating different displays in situations where pilots hands are occupied with flying the aircraft. This paper reports studies on analyzing accuracy of eye gaze controlled interface inside aircraft undertaking representative flying missions. We reported that pilots can undertake representative pointing and selection tasks at less than 2 secs on average. Further, we evaluated the accuracy of eye gaze tracking glass under various G-conditions and analyzed its failure modes. We observed that the accuracy of an eye tracker is less than 5 degree of visual angle up to +3G, although it is less accurate at minus 1G and plus 5G. We observed that eye tracker may fail to track under higher external illumination. We also infer that an eye tracker to be used in military aviation need to have larger vertical field of view than the present available systems. We used this analysis to develop eye gaze trackers for Multi-Functional displays and Head Mounted Display System. We obtained significant reduction in pointing and selection times using our proposed HMDS system compared to traditional TDS.

Vinay Krishna Sharma, L. R. D. Murthy, KamalPreet Singh Saluja et al.

Background: People with severe speech and motor impairment (SSMI) often uses a technique called eye pointing to communicate with outside world. One of their parents, caretakers or teachers hold a printed board in front of them and by analyzing their eye gaze manually, their intentions are interpreted. This technique is often error prone and time consuming and depends on a single caretaker. Objective: We aimed to automate the eye tracking process electronically by using commercially available tablet, computer or laptop and without requiring any dedicated hardware for eye gaze tracking. The eye gaze tracker is used to develop a video see through based AR (augmented reality) display that controls a robotic device with eye gaze and deployed for a fabric printing task. Methodology: We undertook a user centred design process and separately evaluated the web cam based gaze tracker and the video see through based human robot interaction involving users with SSMI. We also reported a user study on manipulating a robotic arm with webcam based eye gaze tracker. Results: Using our bespoke eye gaze controlled interface, able bodied users can select one of nine regions of screen at a median of less than 2 secs and users with SSMI can do so at a median of 4 secs. Using the eye gaze controlled human-robot AR display, users with SSMI could undertake representative pick and drop task at an average duration less than 15 secs and reach a randomly designated target within 60 secs using a COTS eye tracker and at an average time of 2 mins using the webcam based eye gaze tracker.

LRD Murthy, Somnath Arjun, Kamalpreet Singh Saluja et al.

This paper presents development of a smart sensor dashboard for Industry 4.0 encompassing both 2D and 3D visualization modules. In 2D module, we described physical connections among sensors and visualization modules and rendering data on 2D screen. A user study was presented where participants answered a few questions using four types of graphs. We analyzed eye gaze patterns in screen, number of correct answers and response time for all the four graphs. For 3D module, we developed a VR digital twin for sensor data visualization. A user study was presented evaluating the effect of different feedback scenarios on quantitative and qualitative metrics of interaction in the virtual environment. We compared visual and haptic feedback and a multimodal combination of both visual and haptic feedback for VR environment. We found that haptic feedback significantly improved quantitative metrics of interaction than a no feedback case whereas a multimodal feedback is significantly improved qualitative metrics of the interaction.