Abstract
Aiming at the problem of moving target recognition, a moving target tracking model based on FDRIG optical flow is proposed. First, the optical flow equation was analyzed from the theory of optical flow. Then, with the energy functional minimization, the FDRIG optical flow technique was proposed. Taking a road section of a university campus as an experimental section, 30 vehicle motion sequence images were considered as objects to form a vehicle motion sequence image with a complex background. The proposed FDRIG optical flow was used to calculate the vehicle motion optical flow field by the Halcon software. Comparable with the classic Horn and Schunck (HS) and Lucas and Kande (LK) optical flow algorithm, the monitoring results proved that the FDRIG optical flow was highly precise and fast when tracking a moving target. The Ettlinger Tor traffic scene was then taken as the second experimental object; FDRIG optical flow was used to analyze vehicle motion. The superior performance of the FDRIG optical flow was further verified. The whole research work shows that FDRIG optical flow has good performance and speed in tracking moving targets and can be used to monitor complex target motion information in real-time.
Highlights
Moving target tracking is a hotspot of computer image processing research and is widely used to monitor intelligent traffic and robot motion
Lucas and Kande (LK) optical flow algorithm, the monitoring results proved that the FDRIG optical flow was highly precise and fast when tracking a moving target
The waiting vehicles were not tracked by FDRIG optical flow, which indicates that these vehicles were static when the traffic lights turned red
Summary
Moving target tracking is a hotspot of computer image processing research and is widely used to monitor intelligent traffic and robot motion. The main methods for tracking and calculating moving targets are the frame difference method, background subtraction, optical flow method, etc. The optical flow method can provide target recognition, matching and tracking [5,6,7]. It can provide the two-dimensional motion information of the moving target. The optical flow method provides an effective measuring means for vision-based moving target analysis and behavior understanding. Mohamed et al [9] proposed applying local derivative pattern (LDP) features to calculate the optical flow and transform images from gray space to light insensitive space in order. Bao et al [10] presented a fast optical flow method for handling large displacement motions by exploiting the randomized propagation of self-similarity patterns and correspondence offsets
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