Abstract
Recently, video surveillance systems have gained significant interest in several application areas. The examination of video sequences for the detection and tracking of objects remains a major issue in the field of image processing and computer vision. The object detection and tracking process includes the extraction of moving objects from the frames and continual tracking over time. The latest advances in computation intelligence (CI) techniques have become popular in the field of image processing and computer vision. In this aspect, this study introduces a novel computational intelligence-based harmony search algorithm for real-time object detection and tracking (CIHSA-RTODT) technique on video surveillance systems. The CIHSA-RTODT technique mainly focuses on detecting and tracking the objects that exist in the video frame. The CIHSA-RTODT technique incorporates an improved RefineDet-based object detection module, which can effectually recognize multiple objects in the video frame. In addition, the hyperparameter values of the improved RefineDet model are adjusted by the use of the Adagrad optimizer. Moreover, a harmony search algorithm (HSA) with a twin support vector machine (TWSVM) model is employed for object classification. The design of optimal RefineDet feature extraction with the application of HSA to appropriately adjust the parameters involved in the TWSVM model for object detection and tracking shows the novelty of the work. A wide range of experimental analyses are carried out on an open access dataset, and the results are inspected in several ways. The simulation outcome reported the superiority of the CIHSA-RTODT technique over the other existing techniques.