Visual Attention Accelerated Vehicle Detection in Low-Altitude Airborne Video of Urban

Environment

Xianbin Cao, Senior Member, IEEE, Renjun Lin, Pingkun Yan, Senior Member, IEEE,and Xuelong Li, Senior Member, IEEE

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY MARCH 2012

Goal

Outline

• Introduction• Salient region extraction• Obtain regions from saliency map• Classify vehicles• Experiments

Introduction

• For improving road safety and reducing urban traffic congestions caused by the increasing number of vehicles.

• Most of the AVDSs adopt expensive devices suchas infrared cameras , GPS, and high resolution satellite cameras for sensing more information

• Use single optical camera is more efficient.

Introduction

Salient region extraction

Salient region extraction

• For color features• r, g,b,R,G,B,Y seven features• R=r-(g+b)/2 ,G=g-(r+b)/2 B=b-(r+g)/2 , Y=(r+g+b)/3

Salient region extraction

• For orientation features• Use Gabor filters to generate local orientation

feature maps from intensity image I• G(σ, θ, f ) , σ = 2, f = 1 θ as {0°, 45°, 90°, 135°} four features

Salient region extraction

• For motion features• the temporal differences between the current

frame and the three previous frames were computed with intervals of {1, 2, 3}

• Three features

Salient region extraction

• 14 feature maps are computed for salient region extraction

Salient region extraction

• i {0, 1, 2} represents ∈• j {0, 1, 2 ,…} represents the serial numbers∈• operator N(*) normalize

Salient region extraction

Difference without N

Obtain regions from

saliency m

ap

Obtain regions from saliency map

• To effectively obtain the salient regions from the final saliency map, we designed an iterative strategy using inhibition map (IM) and enhancement map (EM).

• IM:avoid picking same area again• EM:enhance regions around the detected

vehicle.

Obtain regions from saliency map

Obtain regions from saliency map

• Filter by size

Classify vehicles

• Use cascaded classifier• 4000 vehicle (positive) samples ,2000 for train

and 2000 for test.• 6000 non-vehicle (negative) samples• All samples scaled to 32*16

Experiments

• Xeon x5660 2.8 GHz computer • 4 GB DDR3-1066• 3 h of video in both the urban and highway

environments• The testing videos of traffic were captured

with the height around 90 m.• size of the video frames is 511×286

Experiments

Experiments

Experiments

• ratio of recall rate (RR) and salient region percentage (SRP), which represents the efficiency of the salient regions extraction, is used as the evaluation criterion.

• High RR/SRP indicates that more vehicles can be covered by less extracted salient regions.

Experiments