A Three-Step Vehicle Detection Framework for Range Estimation Using a Single Camera

Kanjee, Ritesh; Bachoo, Asheer K.; Carroll, Johnson

doi:10.1109/ssci.2015.72

Cited by 3 publications

(2 citation statements)

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“…Hypothesis Generation, Hypothesis Verification and Algorithm Optimization are the three steps to detect and track a leading vehicle [3].Brightness control and pattern matching is used to select preliminary candidate for target vehicles from the captured image by single camera on board. The normalised cross correlation (NCC) method used to match with pre-defined template of vehicles to filter out non-vehicle objects in Hypothesis Generation step[3]. In Hypothesis Verification, a Region of Interest is set using lane detection method where candidates discarded which are not within same lane.…”

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confidence: 99%

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Built-in-self-test system for FPGA-based vehicle video-detection and distance measurement

Anwar¹

2021

Preprint

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Collision of vehicles in highways are very frequent. Because of high speed (more than 100 km/hour), the momentum of collision is too high that leads sever casualty. Automatic Driving Assistance system can assist the vehicle operators to take decision based on realistic practical calculation on safety measures. It is always better to have third eye working parallel with human to avoid road accident. There are several technologies used to develop perfect driving assistance system to achieve higher accuracy in detection, identification and distance measurement of obstacles where vision based system is one of them. Mono-vision system provides cheap and fast solution rather stereo vision. This project work conducted with objective to comprehend computational complexity in implementation of mono-vison camera based object detection where system will generate warning if the detected object has a motion towards target. Processing and analyzing of captured video image is the focused mechanism of implementation and used internal image generator module to mimic actual video camera. Appeared size of the shape of object considered for the decision making. The simulated image pattern can change it’s dimension to represent vehicle movement in one direction (Back and forth). In this work the on-chip car image generation sub-system was proposed designed and partially implemented on the base of the FPGA where Xilinx Zynq-7010 (ZYNQ XC7Z010-1CLG400C) FPGA development board used. Keyword: Computer Vision, mono vision, image processing on FPGA, Automatic Driving Assistance, Vehicle Detection.

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confidence: 99%

“…The ROI shrunk to focus on only target found in previous Hypothesis which helped increase image processing frequency. Perimeter of ROI depends on target size and localization[3].…”

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confidence: 99%