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SamYong Kim, Se-Young Oh, JeongKwan Kang and YoungWoo Ryu

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

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SamYong Kim, Se-Young Oh, JeongKwan Kang and YoungWoo Ryu

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  1. 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, ggr78}@postech.ac.kr • 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, kyongha.park}@samsung.com

  2. system overview

  3. The hardware structure and the test bed

  4. Vehicle detection

  5. Determination of the Day and Night Times • And we calculate the mean intensity M at yellow box

  6. 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

  7. 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

  8. VEHICLE TRACKING IN THE DAY TIME

  9. Generation of On-Line Templates drift problem if updated every frame of tracking • 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 • Where OLT(t) is the online template at frame t and CV is the current vehicle candidate region. OLT(t+1) = aOLT(t) + (1-a) CV a = (DOT-1)/DOT

  10. Template-Based Tracking • p=(p1, p2, p3, p4)Tthat 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)

  11. 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

  12. 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.

  13. EXPERIMENTAL RESULTS

  14. Thank you for your time and attention. HAVE A NICE DAY!

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