Efficient Visual Object Tracking
with Online Nearest Neighbor
Classifier
Many slides adapt from Steve Gu
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.
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
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
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.
Occlusion
Scale change
Background Clutter
Appearance change
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.
-----The main advantages of tracking by detection come from
the flexibility and adaptability of its underlying representation of
appearance.
Tracking-by-detection framework
• Appearance Model
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
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
Tracking with ESS
• We modify the quality function:
• Easy to show that the quality function
satisfies the criteria for branch and bound
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.
Comparison with MIL
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
Changes in view (appearance), scale, occlusion and background
SIFT
Revised
• Feature , from sift to color sift and dense
sift
• Update the background model
Tracking result with dense-sift
How to improve
• Object representation
• Features representation