A Multi-modal Audience Analysis System for Predicting Popularity of Online Videos

Vrochidis, Alexandros; Dimitriou, Nikolaos; Krinidis, Stelios; Panagiotidis, Savvas; Parcharidis, Stathis; Tzovaras, Dimitrios

doi:10.1007/978-3-030-80568-5_38

Cited by 3 publications

(2 citation statements)

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“…The methodology underwent testing both on the generated dataset and was also integrated into the LiveMedia platform [52]. The analysis encompassed videos exceeding 10 h, and user evaluations were conducted to assess the methodology's features [53].…”

Section: Datasetmentioning

confidence: 99%

“…It is used in many areas, including entertainment, health care [1], sports [2], educational content [3], and security [4]. In [5], there is a methodology that fuses features from video content analysis with early viewership statistics of the videos to estimate the video popularity in six months; while in [6], researchers have used similar technologies to create video highlight detection according to user interests that arise automatically by video analytics when watching.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Framework for Monitoring Audience Engagement in Online Video Events

Vrochidis,

Dimitriou,

Krinidis

et al. 2024

Int J Comput Intell Syst

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This paper introduces a deep learning methodology for analyzing audience engagement in online video events. The proposed deep learning framework consists of six layers and starts with keyframe extraction from the video stream and the participants’ face detection. Subsequently, the head pose and emotion per participant are estimated using the HopeNet and JAA-Net deep architectures. Complementary to video analysis, the audio signal is also processed using a neural network that follows the DenseNet-121 architecture. Its purpose is to detect events related to audience engagement, including speech, pauses, and applause. With the combined analysis of video and audio streams, the interest and attention of each participant are inferred more accurately. An experimental evaluation is performed on a newly generated dataset consisting of recordings from online video events, where the proposed framework achieves promising results. Concretely, the F1 scores were 79.21% for interest estimation according to pose, 65.38% for emotion estimation, and 80% for sound event detection. The proposed framework has applications in online educational events, where it can help tutors assess audience engagement and comprehension while hinting at points in their lectures that may require further clarification. It is effective for video streaming platforms that want to provide video recommendations to online users according to audience engagement.

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Section: Datasetmentioning

confidence: 99%