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In recent years, artificial intelligence (AI) based on deep learning (DL) hassparked tremendous global interest. DL is widely used today and has expandedinto various interesting areas. It is becoming more popular in cross-subjectresearch, such as studies of smart city systems, which combine computer sciencewith engineering applications. Human action detection is one of these areas.Human action detection is an interesting challenge due to its stringentrequirements in terms of computing speed and accuracy. High-accuracy real-timeobject tracking is also considered a significant challenge. This paperintegrates the YOLO detection network, which is considered a state-of-the-arttool for real-time object detection, with motion vectors and the CoyoteOptimization Algorithm (COA) to construct a real-time human action localizationand tracking system. The proposed system starts with the extraction of motioninformation from a compressed video stream and the extraction of appearanceinformation from RGB frames using an object detector. Then, a fusion stepbetween the two streams is performed, and the results are fed into the proposedaction tracking model. The COA is used in object tracking due to its accuracyand fast convergence. The basic foundation of the proposed model is theutilization of motion vectors, which already exist in a compressed video bitstream and provide sufficient information to improve the localization of thetarget action without requiring high consumption of computational resourcescompared with other popular methods of extracting motion information, such asoptical flows. This advantage allows the proposed approach to be implemented inchallenging environments where the computational resources are limited, such asInternet of Things (IoT) systems.

A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking