HUMAN MOTION VIDEO DATABASE

MVL (Machine Vision Lab) UIC

HUMAN MOTION VIDEO DATABASE

Jezekiel Ben-ArieECE Department

University Of Illinois at Chicago

Scripts, Queries, Recognition


Composition of interactive motion queries.

Analysis and Recognition of human activities.

Human body parts labeling.

Human detection.


HUMAN ACTIVITY CAPTURE AND REGONITION


Motion Query

Video Retrieval Retrieved videos

Video DatabaseVideos

Video Analysis and Recognition

UserVisual Feedback


HUMAN BODY PART LABELING

Objective: Identify the roles of parts that appear as bars.

Labeling : Using the spatial locations and orientations.

Method : Finding maximum conjunction of partial

hypotheses.


Theoretical Foundations




Illustration of Theoretical Foundations

(a) (b)

Overlap of Spatial distribution for (a) Correct Labeling (b) Incorrect Labeling



(a) (b)

Mesh diagram of Overlap of Spatial distribution for (a) Correct Labeling (b) Incorrect Labeling



Experimental Results

Silhouette Extraction

Bar detectionUsing Gabor signatures.Parsing silhouettes

90 different human poses

98.7% correct labeling.













Illustration of variation of chromaticity and brightness distortion


HUMAN ACTIVITY RECOGNITION

Introduction

Poses indicative of actions taking place

Poses involved in walking

Indexing based recognition using sparse frames Extends this technique with optimal constrained sequencing based voting



Introduction

Temporal sequence of pose vectors

Multidimensional hash tables for model activities

Individual hash tables for each body part

Identifying input pose vectors as samples of densely sampled model activity and create vote vectors

Vote vectors are temporal depiction of the log-likelihood that indexed pose belongs to a model

Dynamic programming based constrained sequencing to recognize activities



Creating Vote Vectors

Illustration of the entire voting process




Videos of sitting action overlaid with skeleton superposed with the help of tracking information

Sparse samples of jump activity adequate for robust recognition




Average votes for 5 test videos of each activity along with the votes for other

activities. Rows – Test Activity

Columns – Model Activity

Recognition rate under various conditions of occlusion




Performance of the approach under conditions of view point variance

MVL (Machine Vision Lab) UICFACE DETECTION

Original Image Skin detection Regions passing the ellipse area criterion

Detection by the Gabors Detected Faces


FACE DETECTION

Original Image Detected faces with medium threshold (0.7)

Detected faces with maximum threshold (0.8)


GUI for Queries Composition

Motion query is composed by using model motion data clips.

An example of a model motion data clip is a walk cycle consisting of a sequence of poses in one basic cycle of left-right steps.

Model motion data clip can also be non-cyclic such as sitting.

Model motion data clip is obtained from a motion capture library or can be interactively composed by the user.


Specify Trajectory Key-points

Interpolate by Splines

Specify Activities

Calculate Segments

Calculate Position and Orientations

Generate Motion Sequences(Scripts)

Display

INTERACTIVE GUI


Theoretical Foundations

• Parameterization of 3-D rotations (Euler Quaternions)• Splines (Catmull Rom)• Interpolation (SLERP, Quaternions)• Human body model• Motion composition techniques

(Inverse Kinematics, Mocap)


Limb Pose Vocabulary


Example of complete body poses


Inverse kinematics based key framing tool


Implementation

Documents

HUMAN MOTION VIDEO DATABASE