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Object tracking survey Object tracking survey Presentation Transcript

  • object tracking:a survey
    Nhat ‘Rich’ Nguyen
    Vision Seminar
    February 2010
    Based on a paper by Yilmaz et al
  • Definition
    2
    Tracking is the problem of estimatingthe trajectory of an object in the image plane as it moves around a scene.
  • Applications
    3
    Motion Recognition
    Automated Surveillance
    Video
    Indexing
    Human Computer Interaction
    Traffic Monitoring
    Vehicle
    Navigation
  • Problems
    Projection
    Noises
    Complex shape
    Complex motion
    Non-rigid
    Occlusions
    Lighting
    Real-time
    4
  • Questions
    Which object representation is suitable?
    Which image features should be used?
    How should motion, appearance of the object be modeled?
    5
    Help you to design
    an object tracking system
  • Overview
    Object Representations
    Features Selection
    Object Detection
    Object Tracking
    Future Direction
    6
  • 1. Object Representation
    7
    [How to represent an object for tracking]
  • 8
    Shape - Points
    Centroid
    Multiple Points
    Control Points
  • 9
    Shape - Patches
    Rectangular Patch
    Elliptical
    Patch
    Multiple Patches
  • 10
    Shape - Contour
    Complete
    Contour
    Skeletal
    Model
    Silhouette
  • 11
    Appearance – Prob. Densities
    Gaussian
    Histogram
    Mixture of
    Gaussians
  • 12
    Appearance – Models
    Geometric
    Template
    Active Contour
    Multi-view Appearance
  • 2. Feature Selection
    13
    [Which feature can be easily distinguished?]
  • 14
    Color
    HSV
    RGB
    LAB
  • 15
    Edges
    Canny Edge Detector
  • 16
    Optical Flow
    Dense field of displacement vectors which defines the translation of each pixel
  • 17
    Texture
    Gray-level Co-occurence Matrix
  • 18
    Texture – Law’s measures
    1-D: kernel for Level, Edge, Spot, Wave, and Ripple
    2-D: convoluting a vertical and a horizontal 1-D kernel
  • 3. Object Detection
    19
    [To track, we first detect.]
  • 20
    Approaches
    Point Detector
    Harris
    SIFT
    Background Subtraction
    Segmentation
    Mean shift
    Graph cuts
    Active Contours
    Supervised Learning
    Adaptive Boosting
    Support Vector Machines
  • 21
    Point Detectors
    Harris
    SIFT
  • 22
    Background Subtraction
  • 23
    Segmentation
  • 24
    Segmentation - Mean shift
  • 25
    Segmentation - Mean shift
  • 26
    Segmentation – Graph-cuts
  • 27
    Segmentation – Active Contour
  • 28
    Supervised Learning
    Learning
    Examples
    Features
    Supervised Learners
    Input
    Classification
  • 29
    Adaptive Boosting
  • 30
    Support Vector Machine
  • 4. Object Tracking
    31
    [State-of-the-art methods.]
  • 32
    Approaches
    Point Tracking
    [Multi-point Correspondence]
    Kernel Tracking
    [Parametric
    Transformation]
    Silhouette Tracking
    [Contour
    Evolution]
  • 33
    Taxonomy
  • 34
    Deterministic
    All possible
    Associations
    Unique
    Associations
    Multi-frame
    Correspondence
    Optimal Assignment Methods:
    Hungarian vs. Greedy
  • 35
    Motion Constraints
    Proximity
    Small change
    in velocity
    Maximum
    Velocity
    Common
    Motion
    Rigidity
  • 36
    Examples
    Rotating dish
    Flying birds
  • 37
    State Estimation
  • Estimate the state of a linear system.
    The state is Gaussian distributed.
    Filters
    38
    Kalman
    The state is NOT Gaussian distributed.
    Particle
    Instead of nearest neighbor, offer a probabilistic approach for data association
    No entering or exiting objects
    Joint Probability
    Data Association
    Multiple
    Hypothesis
    Exhaustively enumerate all possible associations.
  • 39
    Evaluation
  • 40
    Template Matching
    Brute force
    Similarity measure: cross correlation
    - specifies candidate template position
    - object template in previous frame
  • 41
    Mean Shift Tracker
  • 42
    KLT Feature Tracker
    Compute the translation of a rectangular region centered on an interest point.
    Evaluate the quality by computing the affine transformation between corresponding patches.
  • 43
    Eigen Tracker
    Subspace-based approach for multi-view appearance.
    Uses eigenspace for similarity instead of SSD, or correlation.
    Allows distortion in the template.
  • 44
    SVM Tracker
    Positive samples consist of images of the object to be tracked.
    Negative samples consist of images of background object.
    Maximizes the SVM classification score over image region to estimate the object position.
    Knowledge about background object is explicitly incorporated in the tracker.
  • 45
    Evaluation
  • 46
    Shape Matching
    Similar to Template Matching
    Use Hausdorff distance measure to identify most mismatch edges.
    Emphasize parts of model that are not drastically affected by object motion.
    Examples of a person walking : head and torso vs. arms and legs.
  • 47
    State Space Model
    State is term of shape and motion parameters of the contour
    Control points of the contour moves on the spring stiffness parameters
    Measurements consist of the image edges computed in the normal direction of the contour
  • 48
    Gradient Descent
    • Direct minimization algorithm.
    • To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient of the function at the current point.rms and legs.
  • 49
    Contour Evolution
  • 50
    Evaluation
  • 5. Future Direction
    51
    [What’s left for us?]
  • Depth information
    Occlusion Resolution
    Moving cameras
    Non-overlapping view
    Multiple Camera Tracking
    52
  • Broadcast news or home videos.
    Noisy, compressed, unstructured, multiple views.
    Severe occlusion, object partially visible.
    Employ audio in addition to video.
    Unconstrained Videos
    53
  • Ability to learn object model online.
    Unsupervised learning of object models for multiple non-rigid moving object from a single camera.
    Efficient Online Estimation
    54
  • Require detection at some point.
    State-of-the-art tracking methods.
    Point correspondence
    Geometric models
    Contour evolution
    Dependency on context of use.
    Give valuable insight and encourage new research.
    Concluding Remarks
    55