Enhanced the moving object detection and object tracking for traffic surveillance using RBF-FDLNN and CBF algorithm

For traffic surveillance systems (TSS), Moving object detection and tracking is the key technology. On account of moving object-orientation variation, changing weather conditions, moving objects' appearance, and non-target objects in the background, it's very hard to detection some objects in a traffic sequence of the video frame. Though many methods were introduced to detect and track moving objects in a traffic environment, they could not attain a desirable output. Hence, this work proposes an enhanced system for automatic moving object detection (MOD) and object tracking (OT) using RBF-FDLNN and CFR algorithms. The noises existent in a video frame are initially filtered using a GESFA filter. Next, the conversion of the RGB frame to HSL is done, and the background regions are eradicated from the frame using IGMM. After background removal (BGR), the Motion Estimation (ME) of object and OT are performed using GCS and CFR algorithm, respectively. Subsequently, the required features in the frames are extracted and fed into the RBF-FDLNN classifier for performing object detection (OD). The performance of the proposed method RBF-FDLNN classifier is better than other existing methods in the given video frame. Lastly, the outcomes are proffered to corroborate the proposed method's effectiveness.