Summary of Weakly-supervised Anomaly Detection in Surveillance Videos Based on Two-stream I3d Convolution Network, by Sareh Soltani Nejad et al.
Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network
by Sareh Soltani Nejad, Anwar Haque
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method combines Two-Stream Inflated 3D (I3D) Convolutional Networks with Multiple Instance Learning (MIL) for advanced anomaly detection in surveillance videos. This approach outperforms traditional methods by effectively extracting spatial and temporal features, leading to improved precision. The framework prioritizes video clips based on their potential to display anomalies through a ranking mechanism. This strategy enhances accuracy while reducing dependency on manual annotations. Optimized model settings establish new benchmarks in performance and offer a scalable solution for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly detection is important for ensuring public safety in urban surveillance systems. The current method uses Two-Stream Inflated 3D (I3D) Convolutional Networks to improve anomaly detection. This approach works by looking at videos as collections of “bags” that contain instances or short video clips. Each clip is then processed through a ranking mechanism that finds the most important clips, which are likely to show anomalies. This helps make anomaly detection more accurate and reduces the need for manual annotations. |
Keywords
» Artificial intelligence » Anomaly detection » Precision
