Front and Rear Vehicle Detection and Tracking in the Day and Night Times Using Vision and Sonar...
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Transcript of Front and Rear Vehicle Detection and Tracking in the Day and Night Times Using Vision and Sonar...
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
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
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.