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.
Highlights
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 results indicate that the CIHSA-RTODT technique has a superior minimum running time over the other methods
The results indicate that the CIHSA-RTODT technique accomplished optimum results compared with the existing algorithms
Summary
The rapid development of hardware services such as processing machines, smartphones, and cameras has resulted in an explosion of research in automatic video analysis for tracking and detecting objects [1]. Object tracking and detection in a video sequence is a fundamental method in the expansion of different video analysis applications that endeavors to track and detect objects through a series of images by replacing the conventional method of a surveillance camera with a human operator [2]. Object detection needs precise classification of objects in images and requires the precise location of the object, and is an automated image detection system based on geometric and statistical features [3]. The accurateness of object location and object classification is a major indicator to evaluate the efficiency of the detection system. Complex backgrounds and changing light increase the complexity of object detection, for objects in challenging conditions [5]
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