In this paper, an advanced and reliable vehicle detection and tracking technique is proposed and implemented and given the name “Real-Time Vehicle Detection and Tracking” (RT_VDT). The RT_VDT is well suited for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC). The main emphasis of the RT_VDT is the precision and fastness in identifying vehicles on the road and tracking them throughout the drive. In addition, the RT_VDT provides fast enough computation to be embedded in CPUs with affordable GPUs that are currently employed by ADAS systems. The RT_VDT is mainly a pipeline of reliable computer vision algorithms that augment each other and take in raw RGB images to produce the required boundary boxes of the vehicles that appear in the front driving space of the car. Additionally, some of the employed algorithms are working in parallel to strengthen each other in order to produce as accurate as possible output. Each used algorithm is described in details, implemented and its performance is evaluated using actual road images and videos captured by the front mounted camera of the car. The whole pipeline performance is also tested and evaluated on real videos. The evaluation of the proposed technique (RT_VDT) shows that it reliably detects and tracks vehicle boundaries under various conditions. The usefulness and the shortcomings of the proposed technique are also discussed in details, in addition to future projected improvements. © 2019 Institution of Engineering and Technology. All rights reserved.

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