Summary of Real-time Anomaly Detection in Video Streams, by Fabien Poirier
Real-Time Anomaly Detection in Video Streams
by Fabien Poirier
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This thesis develops an artificial intelligence system to detect real-time dangers in video streams by combining temporal and spatial analysis. The proposed architecture integrates object detection, human pose detection, and motion analysis to improve anomaly detection. Techniques from image analysis, such as activation and saliency maps, have been extended to videos, enabling result interpretability. The system uses neural network models like You Only Looks Once (YOLO), Convolutional Recurrent Neuronal Network (CRNN), and multi-layer perceptron for spatial, temporal, and classification tasks, respectively. These models can be combined in parallel or series, with the serial mode being more reliable. Two proprietary datasets were created for training, focusing on potential anomaly-related objects and videos containing anomalies or non-anomalies. This approach enables processing of both continuous video streams and finite videos, providing greater flexibility in detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a system that can detect dangers in live videos. It uses special computer vision techniques to look at the video frame by frame and find unusual things happening. The system is like a superpower that can spot potential problems in real-time. Three different types of neural networks work together to make the system smarter. These networks are trained using two special datasets, one for objects and another for videos with or without anomalies. This allows the system to work with both long and short video streams. The goal is to create a flexible system that can be used in many different situations. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Neural network » Object detection » Yolo
