efficient visual object tracking with online nearest neighbor classifier many slides adapt from...
TRANSCRIPT
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Efficient Visual Object Tracking with Online Nearest Neighbor
Classifier
Many slides adapt from Steve Gu
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Application Fields
• Motion-based recognition ---human identification based on gait;• automated surveillance• --monitoring a scene to detect suspicious activities;• video indexing• --automatic annotation and retrieval of the videos in
multimedia databases;• human-computer interaction -- gesture , eye gaze tracking for data input to
computers;• Robot or vehicle navigation --video-based path planning and obstacle avoidance
capabilities.
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Main contributions• A tracking-by-detection framework is
proposed that combines nearest-neighbor classification of bags of features
• Efficient sub-window search• A framework that handles occlusion,
background clutter, scale and appearance change
State-of-art results on challenging sequences Demo
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Outline
• Object tracking and its challenges
• The proposed tracking-by-detection framework
–Bag-of-Features model
–Online nearest neighbor classifier
–Efficient sub-window search
• Analysis and results
• Result on our data
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Challenges in object tracking
• Occlusion
• Scale change
• Background clutter
• Appearance change
—loss of information from 3D world on a 2D image,—scene illumination changes—complex object motion,—nonrigid or articulated nature of objects,—real-time processing requirements.
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Occlusion
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Scale change
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Background Clutter
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Appearance change
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Main Contributions
• A simple yet effective visual tracker, combine nearest-neighbor classification of bags of features
• A framework that handles occlusion, background clutter, scale and appearance change
• Can be implemented efficiently with ESS.
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-----The main advantages of tracking by detection come from the flexibility and adaptability of its underlying representation of appearance.
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Tracking-by-detection framework
• Appearance Model
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The Objective
Given:
• the object model in the previous frame: Ok-1
• the background modelB, which is static
• the location of the tracked window: Wk
Estimate
• the updated object model: Ok
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The Motion Model
Given• –the object model in the previous frame: Ok-1• –the background model B, which is static• –the window in the previous frame: Wk-1• –the current test window W
Compute• –the matching score between Wand Wk-1given
Ok-1
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Tracking with ESS
• We modify the quality function:
• Easy to show that the quality function satisfies the criteria for branch and bound
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Limitation
•SIFT descriptor cannot handle uniform regions and motion blur
•No advanced motion model is utilized–e.g. Kalmanfilter, particle filter, etc
•current tracker cannot localize objects very precisely when the object’s shape deforms.
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Comparison with MIL
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Application in our project
Application background:
• Robot walks around, taking pictures intermittently; so the
• View, scale of object change when robot is approaching, leaving, walking around the object.
• As robot walking around ,the background changes
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Changes in view (appearance), scale, occlusion and background
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SIFT
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Revised
• Feature , from sift to color sift and dense sift
• Update the background model
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Tracking result with dense-sift
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How to improve
• Object representation
• Features representation