Toward Simulation-Based Training Validation Protocols: Exploring 3d Stereo with Incremental Rehearsal and Partial Occlusion to Instigate and Modulate Smooth Pursuit and Saccade Responses in Baseball Batting

“Keeping your eye on the ball” is a long-standing tenet in baseball batting. And yet, there are no protocols for objectively conditioning, measuring, and/or evaluating eye-on-ball coordination performance relative to baseball-pitch trajectories. Although video games and other virtual simulation technologies offer alternatives for training and obtaining objective measures, baseball batting instruction has relied on traditional eye-pitch coordination exercises with qualitative “face validation”, statistics of whole-task batting performance, and/or subjective batter-interrogation methods, rather than on direct, quantitative eye-movement performance evaluations. Further, protocols for validating transfer-of-training (ToT) for video games and other simulation-based training have not been established in general ― or for eye-movement training, specifically. An exploratory research study was conducted to consider the ecological and ToT validity of a part-task, virtual-fastball simulator implemented in 3D stereo along with a rotary pitching machine standing as proxy for the live-pitch referent. The virtual-fastball and live-pitch simulation couple was designed to facilitate objective eye-movement response measures to live and virtual stimuli. The objective measures 1) served to assess the ecological validity of virtual fastballs, 2) informed the characterization and comparison of eye-movement strategies employed by expert and novice batters, 3) enabled a treatment protocol relying on repurposed incremental-rehearsal and partial-occlusion methods intended to instigate and modulate strategic eye movements, and 4) revealed whether the simulation-based treatment resulted in positive (or negative) ToT in the real task. Results indicated that live fastballs consistently elicited different saccade onset time responses than virtual fastballs. Saccade onset times for live fastballs were consistent with catch-up saccades that follow the smooth-pursuit maximum velocity threshold of approximately 40-70˚/sec while saccade onset times for virtual fastballs lagged in the order of 13%. More experienced batters employed more deliberate and timely combinations of smooth pursuit and catch-up saccades than less experienced batters, enabling them to position their eye to meet the ball near the front edge of home plate. Smooth pursuit and saccade modulation from treatment was inconclusive from virtual-pitch pre- and post-treatment comparisons, but comparisons of live-pitch pre- and post-treatment indicate ToT improvements. Lagging saccade onset times from virtual-pitch suggest possible accommodative-vergence impairment due to accommodation-vergence conflict inherent to 3D stereo displays

Assessment of Driver\u27s Attention to Traffic Signs through Analysis of Gaze and Driving Sequences

A driver’s behavior is one of the most significant factors in Advance Driver Assistance Systems. One area that has received little study is just how observant drivers are in seeing and recognizing traffic signs. In this contribution, we present a system considering the location where a driver is looking (points of gaze) as a factor to determine that whether the driver has seen a sign. Our system detects and classifies traffic signs inside the driver’s attentional visual field to identify whether the driver has seen the traffic signs or not. Based on the results obtained from this stage which provides quantitative information, our system is able to determine how observant of traffic signs that drivers are. We take advantage of the combination of Maximally Stable Extremal Regions algorithm and Color information in addition to a binary linear Support Vector Machine classifier and Histogram of Oriented Gradients as features detector for detection. In classification stage, we use a multi class Support Vector Machine for classifier also Histogram of Oriented Gradients for features. In addition to the detection and recognition of traffic signs, our system is capable of determining if the sign is inside the attentional visual field of the drivers. It means the driver has kept his gaze on traffic signs and sees the sign, while if the sign is not inside this area, the driver did not look at the sign and sign has been missed

Metric Monocular Localization Using Signed Distance Fields

Metric localization plays a critical role in vision-based navigation. For overcoming the degradation of matching photometry under appearance changes, recent research resorted to introducing geometry constraints of the prior scene structure. In this paper, we present a metric localization method for the monocular camera, using the Signed Distance Field (SDF) as a global map representation. Leveraging the volumetric distance information from SDFs, we aim to relax the assumption of an accurate structure from the local Bundle Adjustment (BA) in previous methods. By tightly coupling the distance factor with temporal visual constraints, our system corrects the odometry drift and jointly optimizes global camera poses with the local structure. We validate the proposed approach on both indoor and outdoor public datasets. Compared to the state-of-the-art methods, it achieves a comparable performance with a minimal sensor configuration.Comment: Accepted to 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Activity profiling for minimally invasive surgery

Imperial Users onl

Context-based Visual Feedback Recognition

PhD thesisDuring face-to-face conversation, people use visual feedback (e.g.,head and eye gesture) to communicate relevant information and tosynchronize rhythm between participants. When recognizing visualfeedback, people often rely on more than their visual perception.For instance, knowledge about the current topic and from previousutterances help guide the recognition of nonverbal cues. The goal ofthis thesis is to augment computer interfaces with the ability toperceive visual feedback gestures and to enable the exploitation ofcontextual information from the current interaction state to improvevisual feedback recognition.We introduce the concept of visual feedback anticipationwhere contextual knowledge from an interactive system (e.g. lastspoken utterance from the robot or system events from the GUIinterface) is analyzed online to anticipate visual feedback from ahuman participant and improve visual feedback recognition. Ourmulti-modal framework for context-based visual feedback recognitionwas successfully tested on conversational and non-embodiedinterfaces for head and eye gesture recognition.We also introduce Frame-based Hidden-state Conditional RandomField model, a new discriminative model for visual gesturerecognition which can model the sub-structure of a gesture sequence,learn the dynamics between gesture labels, and can be directlyapplied to label unsegmented sequences. The FHCRF model outperformsprevious approaches (i.e. HMM, SVM and CRF) for visual gesturerecognition and can efficiently learn relevant contextualinformation necessary for visual feedback anticipation.A real-time visual feedback recognition library for interactiveinterfaces (called Watson) was developed to recognize head gaze,head gestures, and eye gaze using the images from a monocular orstereo camera and the context information from the interactivesystem. Watson was downloaded by more then 70 researchers around theworld and was successfully used by MERL, USC, NTT, MIT Media Lab andmany other research groups

A theoretical eye model for uncalibrated real-time eye gaze estimation

Computer vision systems that monitor human activity can be utilized for many diverse applications. Some general applications stemming from such activity monitoring are surveillance, human-computer interfaces, aids for the handicapped, and virtual reality environments. For most of these applications, a non-intrusive system is desirable, either for reasons of covertness or comfort. Also desirable is generality across users, especially for humancomputer interfaces and surveillance. This thesis presents a method of gaze estimation that, without calibration, determines a relatively unconstrained user’s overall horizontal eye gaze. Utilizing anthropometric data and physiological models, a simple, yet general eye model is presented. The equations that describe the gaze angle of the eye in this model are presented. The procedure for choosing the proper features for gaze estimation is detailed and the algorithms utilized to find these points are described. Results from manual and automatic feature extraction are presented and analyzed. The error observed from this model is around 3± and the error observed from the implementation is around 6±. This amount of error is comparable to previous eye gaze estimation algorithms and it validates this model. The results presented across a set of subjects display consistency, which proves the generality of this model. A real-time implementation that operates around 17 frames per second displays the efficiency of the algorithms implemented. While there are many interesting directions for future work, the goals of this thesis were achieved