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 Ryu Department of Electronic and Electrical Engineering Pohang University of Science and Technology Sa31, Hyojadong, Namgu, Pohang, Korea {tripledg, syoh, naroo1, Kwangsoo Kim, Sang-Cheol Park and KyongHa Park Telecommunication R&D Center Samsung Electronics Co., Ltd. Maetan-3dong, Yeongtong-gu, Suwon-city, Korea {kwangsoo72.kim, sangcheol.park,

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  s 2 / 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 ) = a OLT( 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 tracking

Template-Based Tracking p  ( p 1, p 2, p 3, p 4) 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

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