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