Conference proceeding
CPNet: Cross-Parallel Network for Efficient Anomaly Detection
2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
16 Nov 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Anomaly detection in video streams is a challenging problem because of the scarcity of abnormal events and the difficulty of accurately annotating them. To alleviate these issues, unsupervised learning-based prediction methods have been previously applied. These approaches train the model with only normal events and predict a future frame from a sequence of preceding frames by use of encoder-decoder architectures so that they result in small prediction errors on normal events but large errors on abnormal events. The architecture, however, comes with the computational burden as some anomaly detection tasks require low computational cost without sacrificing performance. In this paper, Cross-Parallel Network (CPNet) for efficient anomaly detection is proposed here to minimize computations without performance drops. It consists of N smaller parallel U-Net, each of which is designed to handle a single input frame, to make the calculations significantly more efficient. Additionally, an inter-network shift module is incorporated to capture temporal relationships among sequential frames to enable more accurate future predictions. The quantitative results show that our model requires less computational cost than the baseline U-Net while delivering equivalent performance in anomaly detection.
Metrics
Details
- Title
- CPNet: Cross-Parallel Network for Efficient Anomaly Detection
- Creators
- Youngsaeng Jin - Korea UniversityJonghwan Hong - Korea UniversityDavid Han - Drexel UniversityHanseok Ko - Korea UniversityIEEE
- Publication Details
- 2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000781864800024
- Scopus ID
- 2-s2.0-85124945427
- Other Identifier
- 991019168914704721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Software Engineering
- Engineering, Electrical & Electronic
- Imaging Science & Photographic Technology