Mobile gaze tracking system for outdoor walking behavioral studies

Tomasi, Matteo; Pundlik, Shrinivas; Bowers, Alex R.; Peli, Eli; Luo, Gang

doi:10.1167/16.3.27

Cited by 38 publications

(38 citation statements)

References 37 publications

(48 reference statements)

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“…This will require a mobile eye tracking device integrated with the prosthesis. 35 It will be of particular interest to see if the integration of an eye tracker will improve the performance of patients in orientation and mobility tasks such as sidewalk tracking. 2 Furthermore, with an integrated mobile eye tracker, the participants will be able to be trained for substantial periods of time with this new feature.…”

Section: Discussionmentioning

confidence: 99%

Eye Movement Control in the Argus II Retinal-Prosthesis Enables Reduced Head Movement and Better Localization Precision

Caspi

Roy

Wuyyuru

et al. 2018

Invest. Ophthalmol. Vis. Sci.

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Section: Discussionmentioning

confidence: 99%

Eye Movement Control in the Argus II Retinal-Prosthesis Enables Reduced Head Movement and Better Localization Precision

Caspi

Roy

Wuyyuru

et al. 2018

Invest. Ophthalmol. Vis. Sci.

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“…Significantly, the AOIs did not overlap allowing all gaze allocation, and thus visual engagement within the streets, to be assigned to a single AOI. Coding the data in this way overcame issues regarding eye-movement definition within outdoor moving situations, as the raw eyetracking video was used instead of automated classification of eye-movements as fixations or saccades (Evans et al 2012;Vansteenkiste et al 2015;Tomasi et al 2016). Once coded, a log of sequential dwell durations on the predefined AOIs was exported.…”

Section: Drop Offmentioning

confidence: 99%

Visual engagement with urban street edges: insights using mobile eye-tracking

Simpson

Freeth

Simpson

et al. 2018

Journal of Urbanism: International Research on Placemaking and

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This study provides empirical insight into the extent to which pedestrians visually engage with urban street edges and how social and spatial factors impact such engagement. This was achieved using mobile eye-tracking. The gaze distribution of 24 study participants was systematically recorded as they carried out everyday tasks on differing streets. The findings demonstrated that street edges are the most visually engaged component of streets; that street edge visual engagement is impacted by everyday social tasks as well as the spatial and physical materiality of edges on differing streets; and that street edges, which attract a lot of visual engagement while undertaking optional tasks, also attract greater amounts of visual engagement while undertaking necessary tasks. These findings offer new insight into urban street edge engagement from the direct perspective of street inhabitants and in doing so provide greater understanding of how street edges are experienced.

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“…To ensure its applicability in a wide variety of domains, the Gaze-in-Wild dataset provides easy access to depth of the real world stimulus calibrated from the person's FoV. Contrary to a two IMU system, 17 we chose a single IMU system to avoid using a body worn device since many applications of eye tracking are predominately head-mounted. The hardware setup weighed 700 gms (excluding laptop weight), which is similar to previous setups.…”

Section: Hardware Setup and Error Categorizationmentioning

confidence: 99%

“…Frequent head turns may also lead to an increase in head pose error so it is a good practice to reset the IMU following a few head turns. 17 While we do not hinder participants mid task, pose estimates for certain recordings (marked with γ in Supplementary Table 1) were manually corrected by a rotation operation before and after each heading change during post processing. Head angular drift and deviation in orientation are measured for all participants by the difference in head pose at the beginning and end of a task.…”

Section: Inertial Measurement Unit (Imu)mentioning

confidence: 99%

Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities

Kothari

Yang²,

Kanan³

et al. 2020

Sci Rep

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The interaction between the vestibular and ocular system has primarily been studied in controlled environments. Consequently, off-the shelf tools for categorization of gaze events (e.g. fixations, pursuits, saccade) fail when head movements are allowed. Our approach was to collect a novel, naturalistic, and multimodal dataset of eye+head movements when subjects performed everyday tasks while wearing a mobile eye tracker equipped with an inertial measurement unit and a 3D stereo camera. This Gaze-in-the-Wild dataset (GW) includes eye+head rotational velocities (deg/s), infrared eye images and scene imagery (RGB+D). A portion was labelled by coders into gaze motion events with a mutual agreement of 0.72 sample based Cohen's κ. This labelled data was used to train and evaluate two machine learning algorithms, Random Forest and a Recurrent Neural Network model, for gaze event classification. Assessment involved the application of established and novel event based performance metrics. Classifiers achieve ∼90% human performance in detecting fixations and saccades but fall short (60%) on detecting pursuit movements. Moreover, pursuit classification is far worse in the absence of head movement information. A subsequent analysis of feature significance in our best-performing model revealed a reliance upon absolute eye and head velocity, indicating that classification does not require spatial alignment of the head and eye tracking coordinate systems. The GW dataset, trained classifiers and evaluation metrics will be made publicly available with the intention of facilitating growth in the emerging area of head-free gaze event classification. arXiv:1905.13146v1 [cs.CV] 9 May 2019 (see Section 3). We then use this labelled data for supervised training and assessment of automated event detectors.This work builds upon a variety of techniques previously used to track head orientation during natural behavior. Published studies have demonstrated the use of rotational potentiometers and accelerometers, 8 magnetic coils, 12 or motion capture 13 for the sensing of head orientation. 6 Perhaps the highest precision eye+head tracker which allowed body movement leveraged a 5.8 m 3 custom-made armature capable of generating a pulsing magnetic field. The subject was outfitted with a head-worn receiver capable of measuring head position and orientation within its operational region. 14 Several systems have adopted video based head motion compensation 10, 15 and demonstrated promising results, but are too computationally expensive for real-time use, and are prone to irrecoverable track loss especially during periods of rapid head movement, occlusion of tracking features or degration of image quality due to motion blur. Recent approaches have involved the use of head-mounted IMUs. For example, Larsson et al. used a head-mounted IMU in a study where subjects were asked to perform visual tracking tasks when watching pre-rendered stimuli projected onto a 2D screen. 16 They established that compensating for head movements results in a reduced sta...

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Mobile gaze tracking system for outdoor walking behavioral studies

Cited by 38 publications

References 37 publications

Eye Movement Control in the Argus II Retinal-Prosthesis Enables Reduced Head Movement and Better Localization Precision

Eye Movement Control in the Argus II Retinal-Prosthesis Enables Reduced Head Movement and Better Localization Precision

Visual engagement with urban street edges: insights using mobile eye-tracking

Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities

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