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Application of motion tracking technology in movies, television production and photography using big data

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Abstract

The information era has raised expectations for cinema and television production in recent years. As a result, movie and television production has changed from silent to sound, black-and-white to color, along with more varieties and contents. Therefore, movies, television, and photography use camera motion tracking for effective performance. Based on the benefits and scope of the motion tracking technology, this study uses the aforementioned technology for cinema, television, and photography based on Big data. First, the video-based human motion tracking technology is described in detail, and video motion detection algorithms are introduced. Next, Big data technology is explained and integrated with motion tracking technology in movies and television production along with photography. The method selects the Hadoop open-source cloud platform as the underlying platform and combines the MapReduce distributed programming model and Apache Hadoop Distributed File System (HDFS). Next, it explains the types of real-time tracking technology, lists the operation process of motion tracking by applying it in movies and television production. The particle filter algorithm is employed for human motion tracking in movies, television production and photography technology. This algorithm tracks the initial posture of human body in the video, gathers tracking data, and analyzes human motion information and actions within the recorded footage. Finally, the results of motion tracking algorithms such as Particle Swarm Optimization-Particle Filter (PSO-PF), Particle Filter (PF), Kalman Filter (KF), Mean-Shift (MS) and Deep Neural Network (DNN) are compared and analyzed. The experimental results show that the motion time of a single for the selected algorithm was 84 ms and the tracking success rate was 96.5%, respectively, which indicates that the motion tracking success rate of the selected algorithm is higher. Through further testing of PSO-PF algorithm, the results show that it can process 26.67 Frame Per Second (fps), which is much faster and meets the requirements of movie, television production and photography technology.

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  1. Film-Television and Communication College, Shanghai Normal University, Shanghai, 200233, China

    Li Ni

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  1. Li Ni
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Ni, L. Application of motion tracking technology in movies, television production and photography using big data. Soft Comput 27, 12787–12806 (2023). https://doi.org/10.1007/s00500-023-08963-7

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