speech and gesture corpus from designing to piloting

Speech and Gesture CorpusSpeech and Gesture CorpusFrom Designing to PilotingFrom Designing to Piloting

Gheida Shahrour

Supervised by

Prof. Martin Russell Dr Neil Cooke

Electronic, Electrical and Computer Engineering University of Birmingham

MotivationMotivation

Our research focuses on modelling human behaviour from body motion.

No dataset which could serve our research focus.

Dataset SpecificationDataset Specification

We need data that:

Contains the motion of people’s head, arms and hands

Captured from people come from different cultural backgrounds

Contains spontaneous speech

Captured using a marker-based tracking technique

Why Marker-based Tracking Technique?Why Marker-based Tracking Technique?

Capturing people’s gestures is mainly based on computer vision techniques:• skin colour- people’s skin & light in images.• contour of people- objects may overlap/occluded• tracking from sequence of frames-may not be accurate• images are from 2D- accuracy issues.

To Avoid these problemsWe will capture gestures using marker-based

optical motion tracking:• data obtained from 3D coordinate system• less occlusion & recovered easily• tracking the object accurately- good calibration• tracking the light-reflective markers- accuracy.

Qualisys Track Manager (QTM) Qualisys Track Manager (QTM) The Balance and Posture Laboratory in the School of Psychology

equipped with QTM system (http://www.qualisys.com):

1. 12 cameras with LED strobes which emits a beam of infrared light which is not visible to the naked eye.

2. QTM Software & Analogue Interface for recording speech

3. passive markers- different sizes

4. calibration Kit: axis L shape & wand T shape.

http://www.qualisys.com

Camera & StrobeCamera & Strobe

http://www.qualisys.com

How it works? How it works?

1.The spherical markers are coated with a material to amplify their brightness.

2.The strobes project light towards the markers and the markers reflect it back to the camera

3.Then the camera system measures a 2-dimensional position of the reflective target by combining the 2-D data from several cameras.

4.The camera uses the reflected data from multiple cameras to calculate the 3D position of the markers with high spatial resolution.

.

How it works? How it works?

The Process of Capturing dataThe Process of Capturing data Attach markers on the objects of interest- how? Define the measurement area where subjects will

stand Test the area Calibrate the area Capture your data Save your data

.

Reprocess Data FilesReprocess Data Files Reprocess the files you captured to construct the 3D view-how?

Labeling DataLabeling DataLabel your data – how?

1. Create a text file- Unique name

2. Unique colour

3. Upload the file

4. Drag & drop

5. Play the motion data

6. Play it again

7. Fill the gap

8. Play it again

9. Save the file

10.Export the data

Experiments (1)_Methods & MaterialsExperiments (1)_Methods & Materials

2 volunteers each wears 36 7mm flat-based half spherical markers on:

- head(4) - elbows(2) - waist(4) - golf gloves(26).

12 cameras & measurement volume is not specified

frame rate: 200 frames per second

speech is not recorded.

Experiments (1)3D ViewExperiments (1)3D View

Experiments (1)Best ResultExperiments (1)Best Result

Experiment (2)_MotivationExperiment (2)_Motivation To improve the quality of data.1. Quantity: number of unidentified markers’ trajectories

should be the same number of the markers used in the experiment.

2. Quality: No loss of markers, ghost markers The technique: the reduction both the number of

markers & the measurement volume

Typical 3D Data & Cameras PositionTypical 3D Data & Cameras Position

Low Vs High 3D Tracker ParametersLow Vs High 3D Tracker Parameters Prediction error Residual: the remaining of the trajectory set to low Filling gaps between frames

http://www.qualisys.com

Markers’ Trajectories & Filling the Markers’ Trajectories & Filling the GapGap

Missing DataMissing Data

How to Fill these Gaps?How to Fill these Gaps?

ExperimentsExperiments (2)_Methods & Materials (2)_Methods & Materials

3 volunteers each wears 28 7mm flat-based half spherical markers on:

- head(4) - elbows(2) - shoulders(2) - waist(4) - golf gloves(16)

Experiments (2)_ Measurement VolumeExperiments (2)_ Measurement Volume

Experiments (1)_Cameras PositionExperiments (1)_Cameras Position

ExperimentsExperiments (2)_Cameras Position (2)_Cameras Position

Experiments (2)_SessionsExperiments (2)_Sessions

ExperimentsExperiments (2)_Result (2)_Result

ExperimentsExperiments (2)_Result (2)_Result

ConclusionConclusionWe will track motion of head, arms and handLeave 3 fingers out: middle, ring and pink.Occlusion of the markers on fingers is not only

due to the cameras set up, but also due to the degree of freedom of the hands

Finding unidentified trajectories of markers is laborious and time consuming.

Tracking all fingers is very useful for many applications such as Sign Language but this is not our focus.

Data collection_ assignmentData collection_ assignmentEach volunteer will wear not less than 12mm passive markers on

head(4), elbows(2), waist(4), shoulder(3) and gloves(10)

Group SetupGroup Setup Put yourselves into groups of 3. The members of each group should be from the same first

language, same gender & same country of birth

Each member in British group (country of birth is Britain & first language is English) will record 2 sessions. Each session will last 15 minutes captured in 5 stages. Each stage lasts for 3 minutes.

Each member in the cultural group (country of birth is not Britain & first language is not English) will record 4 sessions. 2 sessions in English as a Second Language and 2 in their first language. Each session will last 15 minutes captured in 5 stages. Each stage lasts for 3 minutes.

Any Question?