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Object tracking survey

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Object tracking survey

Object tracking survey

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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