The problem Live Video and Audio Anomaly Detection System solves

The "Live Video and Audio Anomaly Detection System" addresses several critical issues within the context of live streaming platforms and online content moderation:

1. Real-time Anomaly Detection: By employing advanced video analysis and audio topic modeling techniques, the system can promptly identify and flag live streams containing inappropriate or harmful content, enabling timely intervention to prevent the dissemination of objectionable material.
2. Enhanced User Safety: Through the proactive detection and removal of live videos with undesirable content, the system contributes to fostering a safer online environment for viewers, minimizing potential exposure to harmful, explicit, or inappropriate material.
3. Content Moderation Efficiency: With its automated anomaly detection capabilities, the system streamlines content moderation efforts, reducing the manual workload required for monitoring and flagging objectionable live video and audio content. This efficiency enhancement allows for a more effective and scalable approach to content moderation on live streaming platforms.
4. Regulatory Compliance: By preemptively identifying and halting the broadcast of live videos featuring prohibited content, the system helps live streaming platforms adhere to regulatory standards and guidelines, ensuring legal compliance and mitigating the risk of regulatory penalties and sanctions.
   Overall, the "Live Video and Audio Anomaly Detection System" provides an effective solution for the prompt identification and prevention of harmful content dissemination during live streaming, contributing to a safer and more secure online viewing experience for all users.

Challenges we ran into

In the course of developing the "Live Video and Audio Anomaly Detection System," several challenges emerged, demanding innovative solutions and strategic adaptations:

1. Data Scarcity and Sensitivity: Obtaining an appropriate dataset with live videos containing objectionable content posed a significant initial challenge due to the sensitivity and limited availability of such data. To circumvent this obstacle, the decision was made to temporarily shift the focus to human action recognition using the UCF-101 dataset. This strategic pivot enabled the continuation of model development while addressing the data scarcity issue.
2. Computational Intensity: Dealing with the computational demands of processing large video sizes and complex frame structures presented a notable obstacle. To address this challenge, a transfer learning approach was implemented, leveraging the R(2+1)D model. By adopting transfer learning, the project effectively harnessed pre-existing knowledge and optimized computational resources, enabling efficient model training and analysis within manageable time frames.