A Shape Context Based Car Detection with Hypothesis Pruning

Transkript

Introduction
Related Work
Methodology
Results
A Shape Context Based Car Detection with
Hypothesis Pruning
Işıl Doğa Yakut, Cansın Yıldız, Sefa Kılıç
January 13, 2010
Introduction
Related Work
Methodology
Results
Introduction
Related Work
Methodology
Overview
Top-Down Recognition
Codebook Building
Improved Shape Context
Hypothesis Generation
Hypothesis Pruning
False Positive Elimination
Unification of Related Hypothesis
Results
Introduction
Related Work
Methodology
Results
Car detection and recognition is a key topic where many research
areas; robotics, navigation, surveillance benefit from.
Cars can vary greatly by their:
I shape
I color
I size
I tires
I headlights ...
In our study, a car detection methodology using shape context(SC)
feature is adopted
Introduction
Related Work
Methodology
Results
Existing methodologies for car detection can be categorized as
knowledge-based, motion-based and stereo-based.
Our implementation uses knowledge-based car detection. Here are
several previous work on the subject:
I
Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in
crowded scenes. In: CVPR. (2005)
Introduction
Related Work
Methodology
Results
Existing methodologies for car detection can be categorized as
knowledge-based, motion-based and stereo-based.
Our implementation uses knowledge-based car detection. Here are
several previous work on the subject:
I
Belongie, S., Malik, J., Puzicha, J.: Shape matching and
object recognition using shape contexts. IEEE Trans. Pattern
Anal. Mach. Intell. 24(4) (2002)
Introduction
Related Work
Methodology
Results
Overview
Hypothesis Pruning
The methodology has three parts:
I
I
Codebook Building
I
I
I
Hypothesis Verification
Introduction
Related Work
Methodology
Results
Overview
Hypothesis Pruning
The methodology has three parts:
I
I
Codebook Building
I
I
I
Hypothesis Verification Pruning
Introduction
Related Work
Methodology
Results
Overview
Hypothesis Pruning
Codebook Building
At this stage, using a set of images and their masks, a training is
performed where each feature vector correspond to a codebook
entry.
For point pi , codebook entry cei is as follows;
cei = (ui , δi , mi , wi )
I
ui : shape context feature vector
I
δi : position w.r.t. object center
I
mi : binary mask for patch around pi
I
wi : weight mask of mi
(1)
Introduction
Related Work
Methodology
Results
Overview
Hypothesis Pruning
Shape Context
Shape Context feature describe shapes to allow shape similarity
measuring and point correspondence matching.
I
Select n point on the edges.
I
For each pi of n points, create n − 1 vectors from pi to all
remaining points.
I
Create a histogram using a simple binning scheme:
hi (k) = #{q 6= pi : (q − pi ) ∈ bin(k)}
(2)
Introduction
Related Work
Methodology
Results
Overview
Hypothesis Pruning
I
Angular Blur:
When dense bins are used, even similar images can differ in
histograms.
I
Mask Function:
To eliminate the noise due to background textures masking is
introduced.
Introduction
Related Work
Methodology
Results
Overview
Hypothesis Pruning
Hypothesis Generation Algorithm
1. Compare each shape context(SC) feature with every codebook
entry to predict possible object center.
2. Accumulate matching scores over whole image.
3. Predict points with maximum scores as possible object
centers.
Introduction
Related Work
Methodology
Results
Overview
Hypothesis Pruning
False Positive Elimination
I
Need a way to prune false positives.
I
A threshold value is estimated emprically where it eliminates
the hypothesis with low score values.
I
Threshold score value is estimated as 65.
Introduction
Related Work
Methodology
Results
Overview
Hypothesis Pruning
Unification of Related Hypothesis
I
Improve the hypotheses by unifying related ones
1. Calculate the overlapping area ratios between each pairs of
hypotheses
2. Select the ones having a ratio higher than a threshold and
combine them
Introduction
Related Work
Methodology
Results
Dataset
In order to have a clean training dataset, we have collected over
400 images from Yahoo! Autos. These photos are taken from 5
different view points, and by taking horizontal flips of related
images, we ended up 8 different poses, a total of 720 images.
Introduction
Related Work
Methodology
Results
Sample Results
Introduction
Related Work
Methodology
Results
Sample Results
Introduction
Related Work
Methodology
Results
References
L. Wang, J. Shi, G. Song, and I.-F. Shen: ”Object detection
combining recognition and segmentation,” 2007, pp. 189-199.
Belongie, S., Malik, J., Puzicha, J.: Shape matching and
object recognition using shape contexts. IEEE Trans. Pattern
Anal. Mach. Intell. 24(4) (2002)
Leibe, B., Seemann, E., Schiele, B.: Pedestrian detection in
crowded scenes. In: CVPR. (2005)
Yahoo! Autos, http://autos.yahoo.com/
Introduction
Related Work
Methodology
Results
Any Questions ?

A Shape Context Based Car Detection with Hypothesis Pruning

Transkript

Benzer belgeler

How to Write a Research Proposal

Local Credit:3

CAUMME 2012 International Symposium

Cirsium handaniae (Asteraceae), a new species from Turkey

Letter of Acceptance - INT

analyzing the efficient market hypothesis with the fourier unit root

wood research bending moment capacity of l