A framework for dynamic restructuring of semantic video analysis systems based on learning attention control

Sigari, Mohamad Hoseyn; Soltanian‐Zadeh, Hamid; Pourreza, Hamid Reza

doi:10.1016/j.imavis.2015.07.004

Cited by 5 publications

(1 citation statement)

References 45 publications

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“…They segmented the video based on color, contour, and histogram. The works presented in [3,9,10] first extracted the excitement clip from lengthy videos, and then detected the salient events using histogram and color computations. In addition, these earlier methods often required additional information such as text from score boards and audio commentary related to the game play.…”

Section: Introductionmentioning

confidence: 99%

AI-Driven Salient Soccer Events Recognition Framework for Next-Generation IoT-Enabled Environments

Muhammad

Ullah

Obaidat

et al. 2023

IEEE Internet Things J.

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The salient events recognition of soccer matches in next-generation Internet of things (Nx-IoT) environment aims to analyze the performance of players/teams by the sports analytics and managerial staff. The embedded Nx-IoT devices carried by the soccer players during the match capture and transmit data to an Artificial Intelligence (AI)-assisted computing platform. The interconnectivity of data acquisition devices with an AI-assisted computing platform in the Nx-IoT environment will not only allow the spectators to track the formation of their favorite players during a soccer match but will also enable the managerial staff to evaluate the players' performance in the soccer match as well as in practice sessions. This Nx-IoT-enabled salient event detection feature can be provided to spectators and sports' managerial staff as a financial technology (FinTech) service. In this paper, we propose an efficient deep learning-based framework for multiperson salient soccer events recognition in IoT-enabled FinTech. The proposed framework performs event recognition in three steps: Firstly, image frames are extracted from video streams and resized in the preprocessing step to match the input of the deep network. Secondly, frame-level discriminative features are extracted using a pre-trained convolutional neural network (CNN) architecture. Thirdly, we employ a multi-layer long short-term memory (MLSTM) network to recognize high-level events in soccer videos by exploiting the sequential relation between adjacent frames. Moreover, we introduce a new soccer video events (SVE) dataset containing videos of six salient events of soccer game. To provide a strong baseline, we evaluate our newly created SVE dataset using different traditional machine learning and deep learning algorithms. We also perform event recognition on untrimmed soccer videos using our proposed framework and compare the results with state-of-the-art methods. The obtained results validate the suitability of our proposed framework for salient events recognition in Nx-IoT environments.

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Section: Introductionmentioning

confidence: 99%