Front and Rear Vehicle Detection and Tracking in the Day and Night Times Using Vision and

Sonar Sensor Fusion

SamYong Kim, Se-Young Oh, JeongKwan Kang and YoungWoo RyuDepartment of Electronic and Electrical Engineering Pohang University of Science and Technology Sa31, Hyojadong, Namgu, Pohang, Korea

{tripledg, syoh, naroo1, ggr78}@postech.ac.kr

Kwangsoo Kim, Sang-Cheol Park and KyongHa ParkTelecommunication R&D Center Samsung Electronics Co., Ltd. Maetan-3dong, Yeongtong-gu, Suwon-city, Korea

{kwangsoo72.kim, sangcheol.park, kyongha.park}@samsung.com

system overview

The hardware structure and the test bed

Vehicle detection

Determination of the Day and Night Times

• And we calculate the mean intensity M at yellow box

Vehicle Detection in the Day Time

• Preprocessing

• Vehicle Candidate Extraction

• Vehicle Candidate Validation

• Symmetry rate s2 / n

1. Apply histogram equalization-clear the gap between the dark road and other objects on the road

2. horizontal and vertical scanning filtered noises

3. symmetry rate

1. Apply histogram equalization-clear the gap between the dark road and other objects on the road

2. horizontal and vertical scanning filtered noises

3. symmetry rate

• Vehicle Detection Using Sonar Sensors

• Vehicle Detection at overtaking

not using optical flow at pre-defined ROI

malfunction due to road sign and may miss the long vehicles

so use sonar sensors below 3m

not using optical flow at pre-defined ROI

malfunction due to road sign and may miss the long vehicles

so use sonar sensors below 3m

VEHICLE TRACKING IN THE DAY TIME

Generation of On-Line Templates

• In case of the initial detection and the detection of an overtaking vehicle: Set DOT to 0

• In case of the continuous detection and tracking of the vehicle with the same ID: Increase DOT by 1

• In case of the tracking failure: Decrease DOT by 1

OLT(t+1) = aOLT(t) + (1-a) CV a = (DOT-1)/DOT

• Where OLT(t) is the online template at frame t and CV is the current vehicle candidate region.

drift problem if updated every frame of trackingdrift problem if updated every frame of tracking

Template-Based Tracking

• p(p1, p2, p3, p4)T that represents the transform from the template to the sub-region in the image

• W(x;p) is the warping function

• T(x) is the online template

Lucas-Kanade Algorithm (LKA)

VEHICLE DETECTION IN THE NIGHT TIME

• Small light: Light source by tail lights and brake lights without spreading.

• Large light: Reflected light appeared in a vehicle by other light sources

• Huge light: Light source by headlight

• Small light : light size <= (PW/5)×(PW/5)

• Large light : small light th <= light size <= (PW/2)×(PW/2)

• Huge light : otherwise case

Switchover between Day and Night Times

• Division between the day time image and the night time image is vague

• we apply the two detection methods in an image at the same time and select the one method that creates the vehicle candidate.

• If the both algorithm extract vehicle candidate, we use the algorithm for the day time.

EXPERIMENTAL RESULTS

Thank you for your time and attention.

HAVE A NICE DAY!