MVL (Machine Vision Lab) UIC HUMAN MOTION VIDEO DATABASE Jezekiel Ben-Arie ECE Department University...
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Transcript of MVL (Machine Vision Lab) UIC HUMAN MOTION VIDEO DATABASE Jezekiel Ben-Arie ECE Department University...
MVL (Machine Vision Lab) UIC
HUMAN MOTION VIDEO DATABASE
Jezekiel Ben-ArieECE Department
University Of Illinois at Chicago
Scripts, Queries, Recognition
MVL (Machine Vision Lab) UIC
Composition of interactive motion queries.
Analysis and Recognition of human activities.
Human body parts labeling.
Human detection.
MVL (Machine Vision Lab) UIC
Motion Query
Video Retrieval Retrieved videos
Video DatabaseVideos
Video Analysis and Recognition
UserVisual Feedback
MVL (Machine Vision Lab) UIC
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.
MVL (Machine Vision Lab) UIC
HUMAN BODY PART LABELING
Illustration of Theoretical Foundations
(a) (b)
Overlap of Spatial distribution for (a) Correct Labeling (b) Incorrect Labeling
MVL (Machine Vision Lab) UIC
HUMAN BODY PART LABELING
(a) (b)
Mesh diagram of Overlap of Spatial distribution for (a) Correct Labeling (b) Incorrect Labeling
MVL (Machine Vision Lab) UIC
HUMAN BODY PART LABELING
Experimental Results
Silhouette Extraction
Bar detectionUsing Gabor signatures.Parsing silhouettes
90 different human poses
98.7% correct labeling.
MVL (Machine Vision Lab) UIC
HUMAN BODY PART LABELING
Silhouette Extraction
Illustration of variation of chromaticity and brightness distortion
MVL (Machine Vision Lab) UIC
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
MVL (Machine Vision Lab) UIC
HUMAN ACTIVITY RECOGNITION
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
MVL (Machine Vision Lab) UIC
HUMAN ACTIVITY RECOGNITION
Creating Vote Vectors
Illustration of the entire voting process
MVL (Machine Vision Lab) UIC
HUMAN ACTIVITY RECOGNITION
Experimental Results
Videos of sitting action overlaid with skeleton superposed with the help of tracking information
Sparse samples of jump activity adequate for robust recognition
MVL (Machine Vision Lab) UIC
HUMAN ACTIVITY RECOGNITION
Experimental Results
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
MVL (Machine Vision Lab) UIC
HUMAN ACTIVITY RECOGNITION
Experimental Results
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
MVL (Machine Vision Lab) UIC
FACE DETECTION
Original Image Detected faces with medium threshold (0.7)
Detected faces with maximum threshold (0.8)
MVL (Machine Vision Lab) UIC
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.
MVL (Machine Vision Lab) UIC
Specify Trajectory Key-points
Interpolate by Splines
Specify Activities
Calculate Segments
Calculate Position and Orientations
Generate Motion Sequences(Scripts)
Display
INTERACTIVE GUI
MVL (Machine Vision Lab) UIC
Theoretical Foundations
• Parameterization of 3-D rotations (Euler Quaternions)• Splines (Catmull Rom)• Interpolation (SLERP, Quaternions)• Human body model• Motion composition techniques
(Inverse Kinematics, Mocap)