Object Tracking and Anomaly Detection in Full Motion Video
- Resource Type
- Conference
- Authors
- Zakharov, Igor; Ma, Yue; Henschel, Michael D.; Bennett, John; Parsons, Garrett
- Source
- IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2022 - 2022 IEEE International. :7910-7913 Jul, 2022
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Signal Processing and Analysis
Target tracking
Time series analysis
Object detection
Traffic control
Classification algorithms
Trajectory
Object tracking
Full Motion Video (FMV)
persistent object tracking
anomaly detection
- Language
- ISSN
- 2153-7003
High volume of Full Motion Videos (FMVs) require development of automated tools to help reduce the cognitive burden of the analysts. The number of algorithms for object detection, classification, tracking and anomaly detection in FMV were investigated. The object detection and classification was performed using YOLOv4 technique. Two approaches for object tracking were analyzed: (i) short-term tracking approach based on DeepSORT and (ii) persistent tracking based on template matching and structural similarity index. Anomaly detection and pattern of life analysis algorithms based on trajectory clustering and time series analysis were tested on simulated data and real FMV over a highway with traffic.