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.
Highlights
Object tracking has a vital role in most of the computer vision applications like man-machine interface, robotic vision, and intelligent security system
The results indicate a better performance of the entire system, in terms of accuracy, computational cost, and in handling dynamic scenes than the usual discrete wavelet transform (DWT)-based approaches and the existing dense stereo object tracking approaches
The accuracy of symmetric mask-based discrete wavelet transform (SMDWT)-sum of absolute difference (SAD) correspondence is tested by using the rectified stereo image pairs provided by Middlebury stereo data where dC x, y is the obtained disparity, dT x, y is its ground truth value, and N represents the total number of pixels in the disparity map
Summary
Object tracking has a vital role in most of the computer vision applications like man-machine interface, robotic vision, and intelligent security system. Conventional approaches for moving object detection are background subtraction [3, 4], temporal differencing [5], statistical methods [6], and optical flow method [7, 8]. The symmetric mask-based discrete wavelet transform (SMDWT) is an apt choice to meet these demands, as it is always associated with attractive characteristics such as shorter path, independent subband coding, and fast computation [11, 12]. These approaches are done on monocular videos and it often face challenges such as multiple object occlusion, shadow interference, and radiometric changes. Precise motion detection is accomplished by combining the background subtraction and the frame differencing method, incorporating the advantages of SMDWT
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