Group Abnormal Behavior Detection Based on Fuzzy Clustering
- Resource Type
- Conference
- Authors
- Zhang, Huanhuan; Zhang, Xi; Xie, Jiarun; Wang, Yashen
- Source
- 2020 3rd International Conference on Unmanned Systems (ICUS) Unmanned Systems (ICUS), 2020 3rd International Conference on. :245-250 Nov, 2020
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Computing and Processing
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Robotics and Control Systems
Signal Processing and Analysis
Linear programming
Trajectory
Clustering algorithms
Time measurement
Task analysis
Clustering methods
Principal component analysis
Group Abnormal Behavior Detection
Fuzzy Clustering
Trip Behavior
Spatio-Temporal Data
- Language
For group abnormal behavior analysis, previous methods are easy to be affected by the normal fluctuation characteristics of target or cause “missing” alarm. To overcome these problems, this paper proposes a group abnormal behavior detection method based on fuzzy clustering strategy, to improve the accuracy of abnormal trip behavior measurement and further mine the abnormal targets in the group. Experimental results demonstrate the efficiency of the proposed method on real-world datasets.