Abstract
Performing detection on surveillance videos contributes significantly to the goals of safety and security. However, performing detection on unprotected surveillance video may reveal the privacy of innocent people in the video. Therefore, striking a proper balance between maintaining personal privacy while enhancing the feasibility of detection is an important issue. One promising solution to this problem is to encrypt the surveillance videos and perform detection on the encrypted videos. Most existing encrypted signal processing methods focus on still images or small data volumes; however, because videos are typically much larger, investigating how to process encrypted videos is a significant challenge. In this article, we propose an efficient motion detection and tracking scheme for encrypted H.264/AVC video bitstreams, which does not require the previous decryption on the encrypted video. The main idea is to first estimate motion information from the bitstream structure and codeword length and, then, propose a region update (RU) algorithm to deal with the loss and error drifting of motion caused by the video encryption. The RU algorithm is designed based on the prior knowledge that the object motion in the video is continuous in space and time. Compared to the existing scheme, which is based on video encryption that occurs at the pixel level, the proposed scheme has the advantages of requiring only a small storage of the encrypted video and has a low computational cost for both encryption and detection. Experimental results show that our scheme performs better regarding detection accuracy and execution speed. Moreover, the proposed scheme can work with more than one format-compliant video encryption method, provided that the positions of the macroblocks can be extracted from the encrypted video bitstream. Due to the coupling of video stream encryption and detection algorithms, our scheme can be directly connected to the video stream output (e.g., surveillance cameras) without requiring any camera modifications.
- 2001. CAVIAR Dataset, the EC Funded CAVIAR project/IST 2001. Retrieved from http://homepages.inf.ed.ac.uk/rbf/CAVIAR/. (2001).Google Scholar
- D. Harwood A. M. Elgammal and L. S. Davis. 2000. Non-parametric model for background subtraction. In Proceedings of the European Conference on Computing and Vision, Vol. 4. Springer, 751--767.Google Scholar
- Adnan M. Alattar, Ghassan Al-Regib, Saud Al-Semari et al. 1999. Improved selective encryption techniques for secure transmission of MPEG video bit-streams. In International Conference on Image Processing, Vol. 4. IEEE, 256--260.Google Scholar
- T. Bianchi and A. Piva. 2013. Secure watermarking for multimedia content protection: A review of its benefits and open issues. IEEE Signal Process. Mag. 30, 2 (March 2013), 87--96. DOI:http://dx.doi.org/10.1109/MSP.2012.2228342 Google ScholarCross Ref
- S. Biswas, R. G. Praveen, and R. V. Babu. 2014. Super-pixel based crowd flow segmentation in H.264 compressed videos. In Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP’14). 2319--2323. DOI:http://dx.doi.org/10.1109/ICIP.2014.7025470 Google ScholarCross Ref
- Marianne Celano, Roger Bakeman, Osvaldo Gaytan, Chaundrissa Oyeshiku Smith, Anne Koci, and Sasschon Henderson. 2008. Caregiver depressive symptoms and observed family interaction in low-income children with persistent asthma. Family Process 47, 1 (2008), 7--20. Google ScholarCross Ref
- Kuan-Yu Chu, Yin-Hsi Kuo, and Winston H. Hsu. 2013. Real-time privacy-preserving moving object detection in the cloud. In Proceedings of the ACM Conference on Multimedia. 597--600. Google ScholarDigital Library
- Joan Daemen and Vincent Rijmen. 1999. AES proposal: Rijndael (1999).Google Scholar
- J. Davis and V. Sharma. 2007. Background-subtraction using contour-based fusion of thermal and visible imagery. Comput. Vision Image Understand. 106, 2--3 (2007), 162--182.Google ScholarDigital Library
- James H. Duncan and T.-C. Chou. 1992. On the detection of motion and the computation of optical flow. IEEE Trans. Pattern Anal. Mach. Intell. 14, 3 (Mar 1992), 346--352. Google ScholarDigital Library
- Zekeriya Erkin, Martin Franz, Jorge Guajardo, Stefan Katzenbeisser, Inald Lagendijk, and Tomas Toft. 2009. Privacy-preserving face recognition. In Privacy Enhancing Technologies. Springer, 235--253. Google ScholarDigital Library
- Niels Ferguson, John Kelsey, Stefan Lucks, Bruce Schneier, Michael Stay, David Wagner, and Doug Whiting. 2000. Improved cryptanalysis of Rijndael. In Fse, Vol. 1978. Springer, 213--230.Google Scholar
- Per A. Gustafsson, N.-I. Max Kjellman, and Bengt Björkstén. 2002. Family interaction and a supportive social network as salutogenic factors in childhood atopic illness. Pediatr. Allergy Immunol. 13, 1 (2002), 51--57.Google ScholarCross Ref
- Ankita Lathey and Pradeep K. Atrey. 2015. Image enhancement in encrypted domain over cloud. ACM Trans. Multimedia Computi. Commun. Appl. (TOMM) 11, 3 (2015), 38.Google ScholarDigital Library
- M. Laumer, P. Amon, A. Hutter, and A. Kaup. 2013. Compressed domain moving object detection based on H.264/AVC macroblock types. In Proceedings of the Conference on Computer Vision Imaging and Applications (VISAPP’13).Google Scholar
- M. Laumer, P. Amon, A. Hutter, and A. Kaup. 2015. Compressed domain moving object detection by spatio-temporal analysis of H.264/AVC syntax elements. In Proceedings of the Picture Coding Symposium (PCS’15). 282--286. DOI:http://dx.doi.org/10.1109/PCS.2015.7170091 Google ScholarCross Ref
- Yuan Li, Liwei Liang, Zhaopin Su, and Jianguo Jiang. 2005. A new video encryption algorithm for H.264. In Proceedings of the 2005 5th International Conference on Information, Communications and Signal Processing. IEEE, 1121--1124.Google Scholar
- Shiguo Lian, Zhongxuan Liu, Zhen Ren, and Haila Wang. 2006. Secure advanced video coding based on selective encryption algorithms. IEEE Trans. Cons. Electron. 52, 2 (2006), 621--629. Google ScholarDigital Library
- Shiguo Lian, Jinsheng Sun, Guangjie Liu, and Zhiquan Wang. 2008. Efficient video encryption scheme based on advanced video coding. Multimedia Tools Appl. 38, 1 (2008), 75--89. Google ScholarDigital Library
- Chih-Yang Lin, Kahlil Muchtar, Jia-Ying Lin, Yu-Hsien Sung, and Chia-Hung Yeh. 2016. Moving object detection in the encrypted domain. Multimedia Tools Appl. (2016), 1--25.Google Scholar
- Antoine Manzanera and Julien C. Richefeu. 2007. A new motion detection algorithm based on -- background estimation. Pattern Recogn. Lett. 28, 3 (2007), 320--328. Google ScholarDigital Library
- Pascal Paillier. 1999. Public-key cryptosystems based on composite degree residuosity classes. In Proceedings of the Conference on Advances in Cryptology (EUROCRYPT’99). Springer, 223--238. Google ScholarCross Ref
- Chris Poppe, Sarah De Bruyne, Tom Paridaens, Peter Lambert, and Rik Van de Walle. 2009. Moving object detection in the H.264/AVC compressed domain for video surveillance applications. J. Visual Commun. Image Represent. 20, 6 (2009), 428--437. Google ScholarDigital Library
- Zhenxing Qian, Xinpeng Zhang, and Shuozhong Wang. 2014. Reversible data hiding in encrypted JPEG bitstream. IEEE Trans. Multimedia 16, 5 (2014), 1486--1491. Google ScholarCross Ref
- I. E. G. Richardson. 2003. H.264 and MPEG-4 Video Compression: Video Coding for Next Generation Multimedia. Wiley, Hoboken, NJ. Google ScholarCross Ref
- Ronald L. Rivest, Len Adleman, and Michael L. Dertouzos. 1978. On data banks and privacy homomorphisms. Found. Secure Comput. 4, 11 (1978), 169--180.Google Scholar
- Venkatesh Saligrama and Zhu Chen. 2012. Video anomaly detection based on local statistical aggregates. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2112--2119. Google ScholarCross Ref
- Z. Shahid, M. Chaumont, and W. Puech. 2011. Fast protection of H.264/AVC by selective encryption of CAVLC and CABAC for I and P frames. IEEE Trans. Circuits Syst. Video Technol. 21, 5 (2011), 565--576. DOI:http://dx.doi.org/10.1109/TCSVT.2011.2129090 Google ScholarDigital Library
- George Anastasios Spanos and Tracy Bradley Maples. 1995. Performance study of a selective encryption scheme for the security of networked, real-time video. In Proceedings of the 4th International Conference on Computer Communications and Networks. IEEE, 2--10.Google ScholarCross Ref
- Chris Stauffer and W. E. L. Grimson. 1999. Adaptive background mixture models for real-time tracking. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2, 252. DOI:http://dx.doi.org/10.1109/CVPR.1999.784637 Google ScholarCross Ref
- Douglas R. Stinson. 2005. Cryptography: Theory and Practice. CRC press.Google ScholarCross Ref
- T. Stutz and A. Uhl. 2012. A survey of H.264 AVC/SVC encryption. IEEE Trans. Circuits Syst. Video Technol. 22, 3 (2012), 325--339. DOI:http://dx.doi.org/10.1109/TCSVT.2011.2162290 Google ScholarDigital Library
- K. Szczerba, S. Forchhammer, J. Stottrup-Andersen, and P. T. Eybye. 2009. Fast compressed domain motion detection in H.264 video streams for video surveillance applications. In Proceedings of the 6th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS’09). 478--483. DOI:http://dx.doi.org/10.1109/AVSS.2009.78 Google ScholarDigital Library
- Nithin M. Thomas, Damien Lefol, David R. Bull, and David Redmill. 2007. A novel secure H.264 transcoder using selective encryption. In Proceedings of the IEEE International Conference on Image Processing (ICIP’07). Vol. 4. IEEE, IV--85.Google ScholarCross Ref
- Christopher Thompson and David Wagner. 2016. Securing recognizers for rich video applications. In Proceedings of the 6th Workshop on Security and Privacy in Smartphones and Mobile Devices. ACM, 53--62.Google ScholarDigital Library
- M. Tom and R. V. Babu. 2013. Fast moving-object detection in H.264/AVC compressed domain for video surveillance. In Proceedings of the 2013 4th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG’13). 1--4. DOI:http://dx.doi.org/10.1109/NCVPRIPG.2013.6776202 Google ScholarCross Ref
- Du-Ming Tsai and Shia-Chih Lai. 2009. Independent component analysis-based background subtraction for indoor surveillance. IEEE Trans. Image Process. 18, 1 (Jan 2009), 158--167. DOI:http://dx.doi.org/10.1109/TIP.2008.2007558 Google ScholarDigital Library
- Maneesh Upmanyu, Anoop M. Namboodiri, Kannan Srinathan, and C. V. Jawahar. 2009. Efficient privacy preserving video surveillance. In Proceedings of the IEEE 12th International Conference on Computer Vision. 1639--1646. Google ScholarCross Ref
- Antoine Vacavant, Lionel Robinault, Serge Miguet, Chris Poppe, and Rik Van de Walle. 2011. Adaptive background subtraction in H. 264/AVC bitstreams based on macroblock sizes. In Proceedings of the Conference on Computer Vision Imaging and Applications (VISAPP’11). 51--58.Google Scholar
- Shuang Wang and Shuqiang Jiang. 2015. INSTRE: A new benchmark for instance-level object retrieval and recognition. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 11, 3 (2015), 37.Google Scholar
- Dawen Xu, Rangding Wang, and Yun Q. Shi. 2014. Data hiding in encrypted H.264/AVC video streams by codeword substitution. IEEE Trans. Info. Forensics Security (2014), 596--606.Google Scholar
- Xiaoshan Yang, Tianzhu Zhang, and Changsheng Xu. 2016. Semantic feature mining for video event understanding. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 12, 4 (2016), 55.Google ScholarDigital Library
- T. Yokoyama, T. Iwasaki, and T. Watanabe. 2009. Motion vector based moving object detection and tracking in the MPEG compressed domain. In Proceedings of the 7th International Workshop on Content-Based Multimedia Indexing (CBMI’09). 201--206. DOI:http://dx.doi.org/10.1109/CBMI.2009.33 Google ScholarDigital Library
Index Terms
- An Efficient Motion Detection and Tracking Scheme for Encrypted Surveillance Videos
Recommendations
Efficient certificate-based verifiable encrypted signature scheme
Certificate-based public key cryptographic is a novel cryptographic primitive solving the heavy management problem in the conventional public key cryptographic. Verifiable encrypted signature is useful for many cryptographic protocols and often is used ...
An efficient secure proxy verifiably encrypted signature scheme
Verifiably encrypted signature is an important cryptographic primitive, it can convince a verifier that a given ciphertext is an encryption of signature on a given message. It is often used as a building block to construct an optimistic fair exchange. ...
A robust verifiably encrypted signature scheme
EUC'06: Proceedings of the 2006 international conference on Emerging Directions in Embedded and Ubiquitous ComputingA verifiably encrypted signature can convince the verifier that a given cipher-text is the encryption of a signature on a given message. It is often used as a building block to construct optimistic fair exchange. Recently, Gu et.al gave an ID-based ...
Comments