Log-polar based framework for mobile vehicle tracking with road follower

A new computationally efficient framework for vehicle tracking on a mobile platform is proposed. The principal component of the framework is the log-polar transformation applied to video frames captured from a standard uniformly sampled format camera. The log-polar transformation provides two major benefits to real-time vehicle tracking from a mobile vehicle platform moving along a single or multi-lane road. First, it significantly reduces the amount of data required to be processed since it collapses the original Cartesian video frames into log-polar images with much smaller dimensions. Second, the log-polar transformation is capable of mitigating perspective distortion due to its scale invariance property. This second aspect is of interest for vehicle tracking because the target vehicle appearance is preserved for all distances from the observer (camera). This works however only if the center of log-polar transformation is coincident with the vanishing point of perspective view. Therefore, a road following algorithm is proposed to keep the center of log-polar transform on the vanishing point at every video frame compensating for the carrying vehicle movements. Since the algorithm is intended to be used in the mobile embedded devices, it is developed to achieve both mathematical simplicity and algorithmic efficiency while avoiding computationally expensive mathematical functions. The use of trigonometric and exponential functions is minimized comparing to the log-Hough transform traditionally used in log-polar space. This new algorithm focuses on straight radial line fragments, thus shifting its mathematical engine to the linear equations’ domain.