Illumination Invariant Motion Detection and Tracking Using SMDWT and a Dense Disparity-Variance Method

Abstract

The navigation management systems in autonomous vehicles should be able to gather solid information about the immediate environment of the vehicle, discern ambulance from a delivery truck, and react in a proper manner to handle any difficult situation. Separating such information from a vision controlled system is a computationally demanding task for heavy traffic areas in the real world environmental conditions. In such a scenario, we need a robust moving object detection tracking system. To achieve this, we can make use of stereo vision-based moving object detection and tracking, utilizing symmetric mask-based discrete wavelet transform to deal with illumination changes, low memory requirement, and fake motion avoidance. The accurate motion detection in complex dynamic scenes is done by the combined background subtraction and frame differencing technique. For the fast motion track, we can employ a dense disparity-variance method. This SMDWT-based object detection has a maximum and minimum accuracy of 99.62% and 94.95%, respectively. The motion track has the highest accuracy of 79.47% within the time frame of 28.03 seconds. The lowest accuracy of the system is 62.01% within the time frame of 34.46 seconds. From the analysis, it is clear that this proposed method exceptionally outperforms the existing monocular and dense stereo object tracking approaches in terms of low computational cost, high accuracy, and in handling the dynamic environments.