1 research outputs found
A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking
In recent years, artificial intelligence (AI) based on deep learning (DL) has
sparked tremendous global interest. DL is widely used today and has expanded
into various interesting areas. It is becoming more popular in cross-subject
research, such as studies of smart city systems, which combine computer science
with engineering applications. Human action detection is one of these areas.
Human action detection is an interesting challenge due to its stringent
requirements in terms of computing speed and accuracy. High-accuracy real-time
object tracking is also considered a significant challenge. This paper
integrates the YOLO detection network, which is considered a state-of-the-art
tool for real-time object detection, with motion vectors and the Coyote
Optimization Algorithm (COA) to construct a real-time human action localization
and tracking system. The proposed system starts with the extraction of motion
information from a compressed video stream and the extraction of appearance
information from RGB frames using an object detector. Then, a fusion step
between the two streams is performed, and the results are fed into the proposed
action tracking model. The COA is used in object tracking due to its accuracy
and fast convergence. The basic foundation of the proposed model is the
utilization of motion vectors, which already exist in a compressed video bit
stream and provide sufficient information to improve the localization of the
target action without requiring high consumption of computational resources
compared with other popular methods of extracting motion information, such as
optical flows. This advantage allows the proposed approach to be implemented in
challenging environments where the computational resources are limited, such as
Internet of Things (IoT) systems.Comment: SUBMITTED TO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING
SYSTEM