Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video

Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for enc...

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Vydáno v:IEEE transactions on information forensics and security Ročník 16; s. 5381 - 5396
Hlavní autoři: Tian, Xianhao, Zheng, Peijia, Huang, Jiwu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptive algorithms
boldsymbol Encrypted video processing
Cloud computing
Clustering
Codec
compressed-domain feature
Cryptography
Domains
Encryption
Feature extraction
Kalman filters
Motion detection
Motion perception
Moving object recognition
Multiple target tracking
object tracking
Privacy
Robustness
Segmentation
Servers
Streaming media
Surveillance
Surveillance systems
Vibration
Video compression
video surveillance
ISSN:1556-6013, 1556-6021
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Shrnutí:Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for encrypted video moving object detection and tracking, none has robust performance against complex and dynamic scenes. In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams. By analyzing the properties of the video codec and format-compliant encryption schemes, we propose a new compressed-domain feature to capture motion information in complex surveillance scenarios. Based on this feature, we design an adaptive clustering algorithm for moving object segmentation with an accuracy of <inline-formula> <tex-math notation="LaTeX">4\times 4 </tex-math></inline-formula> pixels. We then propose a multiple object tracking scheme that uses Kalman filter estimation and adaptive measurement refinement. The proposed scheme does not require video decryption or full decompression and has a very low computation load. The experimental results demonstrate that our scheme achieves the best detection and tracking performance compared with existing works in the encrypted and compressed domain. Our scheme can be effectively used in complex surveillance scenarios with different challenges, such as camera movement/jitter, dynamic background, and shadows.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2021.3128817