Abstract
Localization of suspicious moving objects in dynamic environments requires high accuracy mapping. A deep learning model is proposed to track crossing moving objects in the opposite direction. Moving objects locus measurements are computed from the space included in the boundaries of the images in the intersecting cameras. Object appearance is designated by the color and textural histograms in the intersecting camera views. The incorrect mapping of moving objects in a dynamic environment through synchronized localization can be considerably increased in complex areas. This is done due to the presence of unfit points that are triggered by moving targets. To face this problem, a robust model using the dynamic province rejection technique (DPR) is presented. We are proposing a novel model that incorporates a combination of the deep learning method and a tracking system that rejects dynamic areas which are not within the environment boundary of interest. The technique detects the dynamic points from sequential video images and partitions the current video image into super blocks and tags the border differences. In the last stage, dynamic areas are computed from dynamic points and superblock boundaries. Static regions are utilized to compute the positions to enhance the path computation precision of the model. Simulation results show that the introduced model has better performance than the state-of-the-art similar models in both the VID and MOVSD4 datasets and is higher than the state-of-the-art tracking systems with better speed performance. The experiments prove that the computed path error in the dynamic setting can be decreased by 81%.