Tytuł artykułu
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
In recent years, a large number of cameras have been installed in public spaces as a part of intelligent video surveillance systems. Such systems are being continuously developed due to the advancements in the Video Content Analysis algorithms. In this paper, some of the latest state-of-the-art intelligent video surveillance systems will be presented in the context of their most desirable characteristics and features. Due to the variety of the solutions the following categories have been taken into consideration: systems based on object detection, tracking and movement analysis, systems able to warn against, detect and identify abnormal and alarming situations, systems based on vehicle detection and traffic or parking lots analysis, object counting systems, systems based on multiple integrated camera views, privacy preserving systems and systems based on cloud environment. The paper describes several solutions for each category and underlines main functionalities of the current intelligent surveillance systems.
Słowa kluczowe
Rocznik
Tom
Strony
13--27
Opis fizyczny
Bibliogr. 67 poz.
Twórcy
autor
- West Pomeranian University of Technology, Faculty of Computer Science and Information Technology, Żołnierska 52, 71-210, Szczecin, Poland
- Smart Monitor sp. z o. o., Cyfrowa 6, 71-441, Szczecin, Poland
autor
- West Pomeranian University of Technology, Faculty of Computer Science and Information Technology, Żołnierska 52, 71-210, Szczecin, Poland
- Smart Monitor sp. z o. o., Cyfrowa 6, 71-441, Szczecin, Poland
autor
- West Pomeranian University of Technology, Faculty of Computer Science and Information Technology, Żołnierska 52, 71-210, Szczecin, Poland
autor
- Smart Monitor sp. z o. o., Cyfrowa 6, 71-441, Szczecin, Poland
Bibliografia
- [1] Frejlichowski, D., Forczmański, P., Nowosielski, A., Gościewska, K., Hofman, R.: SmartMonitor: An Approach to Simple, Intelligent and Affordable Visual Surveillance System. In: Bolc, L. et al. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 726–734. Springer, Heidelberg, 2012.
- [2] Frejlichowski, D., Gościewska, K., Forczmański, P., Nowosielski, A., Hofman, R.: Extraction of the Foreground Regions by Means of the Adaptive Background Modelling Based on Various Colour Components for a Visual Surveillance System. In: Burduk, R. et al. (eds.) CORES 2013. Advances in Intelligent Systems and Computing, vol. 226, pp. 351–360. Springer International Publishing, 2013.
- [3] Frejlichowski, D., Gościewska, K., Forczmański, P., Hofman, R.: ’SmartMonitor’ — An Intelligent Security System for the Protection of Individuals and Small Properties with the Possibility of Home Automation. Sensors 14, 9922–9948, 2014.
- [4] Frejlichowski, D., Gościewska, K., Forczmański, P., Hofman, R.: Application of foreground object patterns analysis for event detection in an innovative video surveillance system. Pattern Anal. Appl., 1–12, 2014.
- [5] Singh, V., Kankanhalli, M.: Adversary aware surveillance systems, IEEE Trans. Inf. Forensics Security, vol. 4, no. 3, pp. 552–563, Sep. 2009.
- [6] Elliott, D.: Intelligent video solution: A definition, Security, pp. 46–48, 2010.
- [7] Avidan, S.: Ensemble tracking, IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 2, pp. 261– 271, Feb. 2007.
- [8] Khan, Z., Gu, I.: Joint feature correspondences and appearance similarity for robust visual object tracking, IEEE Trans. Inf. Forensics Security, vol. 5, no. 3, pp. 591–606, Sep. 2010.
- [9] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection, in Proc. CVPR, 2005, pp. 886–893.
- [10] Wang, L.: Abnormal walking gait analysis using silhouette-masked flow histograms, in Proc. ICPR, 2006, vol. 3, pp. 473–476.
- [11] Wang, S., Lee, H.: A cascade framework for a real-time statistical plate recognition system, IEEE Trans. Inf. Forensics Security, vol. 2, no. 2, pp. 267–282, Jun. 2007.
- [12] Yu, X., Chinomi, K., Koshimizu, T., Nitta, N., Ito, Y., Babaguchi, N.: Privacy protecting visual processing for secure video surveillance, in Proc. ICIP, 2008, pp. 1672–1675.
- [13] Park, U., Jain, A.: Face matching and retrieval using soft biometrics, IEEE Trans. Inf. Forensics Security, vol. 5, no. 3, pp. 406–415, Sep. 2010.
- [14] Cong, Y., Yuan, J., Luo, J.: Towards scalable summarization of consumer videos via sparse dictionary selection, IEEE Trans.Multimedia, vol. 14, no. 1, pp. 66–75, Feb. 2012.
- [15] Cong, Y., Gong, H., Zhu, S., Tang, Y.: Flow mosaicking: Real-time pedestrian counting without scene-specific learning, in Proc. CVPR, 2009, pp. 1093–1100.
- [16] Venetianer, P. L., Deng, H. L.: Performance evaluation of an intelligent video surveillance system—A case study, Comput. Vis. Image Understanding, vol. 114, no. 11, pp. 1292–1302, 2010.
- [17] Paul, M., Haque, S., Chakraborty, S.: Human detection in surveillance videos and its applications – a review. EURASIP Journal on Advances in Signal Processing 176, 2013.
- [18] Nguyen, T.-H.-B., Kim, H.: Novel and efficient pedestrian detection using bidirectional PCA. Pattern Recognition 46, pp. 2220-2227, 2013.
- [19] Hu, M.-C., Cheng, W.-H., Hu, C.-S., Wu, J.-L., Li, J.-W.: Efficient human detection in crowded environment. Multimedia Systems., 2014.
- [20] Hu, C.-S., Hu, M.-C., Cheng, W.-H., Wu, J.-L.: Efficient human detection in crowded environment based on motion and appearance information. 5th International Conference on Internet Multimedia Computing and Service, ICIMCS 2013, pp. 97-100. Huangshan, China., 2013.
- [21] Park, J.-H., Shin, Y.-C., Jeong, J.-W., Lee, M.-J.: Detection and Tracking of Intruding Objects based on Spatial and Temporal Relationship of Objects. ASTL(21), pp. 271-274, 2013.
- [22] Zhang, D., Peng, H., Haibin, Y., Lu, Y.: Crowd Abnormal Behaviour Detection Based on Machine Learning. Information Technology Journal, 12(6), pp. 1199-1205, 2013.
- [23] Cong, Y., Yuan, J., Tang, Y.: Video Anomaly Search in Crowded Scenes via Spatio-temporal Motion Context. IEEE Transactions on Information Forensics and Security, 8(10), pp. 1590-1599, 2013.
- [24] Vishwakarma, S., Agrawal, A.: A survey on activity recognition and behaviour understanding in video surveillance. The Visual Computer, 29(10), pp. 983-1009, 2013.
- [25] Hu, Q., Qin, L., Huang, Q.-M.: A survey on visual human action recognition. Chinese Journal of Computers, 36, pp. 2512-2524, 2013.
- [26] Borges, P., Conci, N., Cavallaro, A. Video-based human behavior understanding: A survey. IEEE Transactions on Circuits and Systems for Video Technology, 11, pp. 1993-2008, 2013.
- [27] Blunsden, S., Fisher, R. Pre-fight detection: Classification of fighting situations using hierarchical AdaBoost. Fourth International Conference on Computer Vision Theory and Applications, 2, pp. 303-308, 2009.
- [28] Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Robust Video Surveillance for Fall Detection Based on Human Shape Deformation. IEEE Transactions on Circuits and Systems for Video Technology, 21(5), pp. 611-622, 2011.
- [29] Ngo, Y., Nguyen, H., Pham, T.: Study on fall detection based on intelligent video analysis. The 2012 International Conference on Advanced Technologies for Communications, ATC 2012, 2012.
- [30] Albusac, J., Vallejo, D., Jimenez-Linares, L., Castro-Schez, J., Rodriguez-Benitez, L.: Intelligent surveillance based on normality analysis to detect abnormal behaviors. International Journal of Pattern Recognition and Artificial Intelligence, 23(7), pp. 1223-1244, 2009.
- [31] Suriani, N., Hussain, A., Zulkifley, M.: Sudden event recognition: a survey. Sensors, 13(8), pages 9966-9998, 2013.
- [32] Karpagavalli, P., Ramprasad, A.: Estimating the density of the people and counting the number of people in a crowd environment for human safety. 2nd International Conference on Communication and Signal Processing, ICCSP 2013, pp. 663-667, 2013.
- [33] Conte, D., Foggia, P., Percannella, G., Vento, M.: Counting moving persons in crowded scenes. Machine Vision and Applications, pp. 1029-1042, 2013.
- [34] Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., Van Den Hengel, A.: A survey of appearance models in visual object tracking. ACM Transactions on Intelligent Systems and Technology, 4(4)., 2013.
- [35] Morioka, K., Kovacs, S., Joo-Ho, L., Korondi, P.: A cooperative object tracking system with fuzzy-based adaptive camera selection. International Journal on Smart Sensing and Intelligent Systems, pages 338-358, 2010.
- [36] Liu, Y., Lu, Y., Shi, Q., Ding, J.: Optical flow based urban road vehicle tracking. Ninth International Conference on Computational Intelligence and Security, pp. 391-395. Beijing, China., 2013.
- [37] Jiang, W., Xiao, C., Jin, H., Zhu, S., Lu, Z.: Vehicle Tracking with Non-overlapping Views for Multi-camera Surveillance System. 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing (HPCC_EUC), pp. 1213-1220, 2013.
- [38] Çetin, A. E., Dimitropoulos, K., Gouverneur, B., Grammalidis, N., Günay, O., Habiboglu, Y. H., Verstockt, S.: Video fire detection – Review. Digital Signal Processing, 23, pp. 1827-1843, 2013.
- [39] Jiang, B., Lu, Y., Li, X., Lin, L.: Towards a solid solution of realtime fire and flame detection. Multimedia Tools and Applications., 2014.
- [40] Li, Z., Li, Q.: Protection of regional object and camera tampering. Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, pp. 672-675. Beijing, China., 2013.
- [41] Muchtar, K., Lin, C.-Y., Kang, L.-W., Yeh, C.-H.: Abandoned object detection in complicated environments. 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013. Kaohsiung; Taiwan., 2013.
- [42] Miyahara, A., Nagayama, I.: An intelligent security camera system for kidnapping detection. Journal of Advanced Computational Intelligence and Intelligent Informatics, 17, pp. 746-752, 2013.
- [43] Barhm, M., Qwasmi, N., Qureshi, F., el Khatib, K.: Negotiating Privacy Preferences in Video Surveillance Systems. W K. Mehrotra, C. Mohan, J. Oh, P. Varshney, and M. Ali (Editors), Modern Approaches in Applied Intelligence, vol. 6704, pp. 511-521. Springer Berlin Heidelberg., 2011.
- [44] Saini, M., Atrey, P., Mehrotra, S., Kankanhalli, M.: W3-privacy: understanding what, when, and where inference channels in multi-camera surveillance video. Multimedia Tools and Applications, 68(1), pp. 135-158, 2014.
- [45] Zhang, P., Thomas, T., Emmanuel, S.: Privacy enabled video surveillance using a two state Markov tracking algorithm. Multimedia Systems, 18(2), 175-199, 2012.
- [46] Cancela, B., Ortega, M., Penedo, M.: Multiple human tracking system for unpredictable trajectories. Machine Vision and Applications, 25(2), 511-527, 2014.
- [47] Tathe, S., Narote, S.: Real-time human detection and tracking. 2013 Annual IEEE India Conference (INDICON), pp. 1-5, 2013.
- [48] Kushwaha, A., Sharma, C., Khare, M., Srivastava, R., Khare, A.: Automatic multiple human detection and tracking for visual surveillance system. 2012 International Conference on Informatics, Electronics Vision (ICIEV), pp. 326-331, 2012.
- [49] Andersson, M., Gustafsson, F., St-Laurent, L., Prevost, D.: Recognition of Anomalous Motion Patterns in Urban Surveillance. IEEE Journal of Selected Topics in Signal Processing, 7(1), 102-110, 2013.
- [50] Kiryati, N., Raviv, T., Ivanchenko, Y., Rochel, S.: Real-time abnormal motion detection in surveillance video. 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1-4, 2008.
- [51] Calavia, L., Baladrón, C., Aguiar, J. M., Carro, B., Esguevillas, A. S.: A Semantic Autonomous Video Surveillance System for Dense Camera Networks in Smart Cities. Sensors, 10407-10429, 2012.
- [52] Wang, J., Xu, Z. STV-based video feature processing for action recognition. Signal Processing, 93(8), 2151-2168, 2013.
- [53] Lim, M. K., Tang, S., Chan, C. S. iSurveillance: Intelligent framework for multiple events detection in surveillance videos. Expert Systems with Applications, 41(10), 4704-4715, 2014.
- [54] Lee, S., Nevatia, R.: Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system. Machine Vision and Applications, 25(1), 133-143, 2014.
- [55] Su, H., Yang, H., Zheng, S., Fan, Y., Wei, S.: The Large-Scale Crowd Behavior Perception Based on Spatio-Temporal Viscous Fluid Field. IEEE Transactions on Information Forensics and Security, 8(10), pp. 1575-1589, 2013.
- [56] Park, S., Yoo, C.: Video scene analysis and irregular behavior detection for intelligent surveillance system. 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 577-581, 2012.
- [57] Onal, I., Kardas, K., Rezaeitabar, Y., Bayram, U., Bal, M., Ulusoy, I., Cicekli, N.: A framework for detecting complex events in surveillance videos. 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1-6, 2013.
- [58] Santos, M., Linder, M., Schnitman, L., Nunes, U., Oliveira, L.: Learning to segment roads for traffic analysis in urban images. 2013 IEEE Intelligent Vehicles Symposium (IV), pp. 527-532, 2013.
- [59] Mossi, J., Albiol, A., Albiol, A., Ornedo, V.: Real-time traffic analysis at night-time. 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 2941-2944, 2011.
- [60] Bulan, O., Loce, R. P., Wu, W., Wang, Y., Bernal, E. A., Fan, Z. Video-based real-time onstreet parking occupancy detection system. Journal of Electronic Imaging, 22(4), 41109-41109, 2013.
- [61] Rao, Y.: Automatic vehicle recognition in multiple cameras for video surveillance. The Visual Computer, 1-10, 2014.
- [62] Zhou, Y., Luo, J.: A practical method for counting arbitrary target objects in arbitrary scenes. 2013 IEEE International Conference on Multimedia and Expo (ICME), pp. 1-6, 2013.
- [63] Li, J., Huang, L., Liu, C.: An efficient self-learning people counting system. 2011 First Asian Conference on Pattern Recognition (ACPR), pp. 125-129, 2011.
- [64] Chen, Y.-Y., Huang, Y.-H., Cheng, Y.-C., Chen, Y.-S.: A 3-D surveillance system using multiple integrated cameras. 2010 IEEE International Conference on Information and Automation (ICIA), pp. 1930-1935, 2010.
- [65] Castiglione, A., Cepparulo, M., De Santis, A., Palmieri, F.: Towards a Lawfully Secure and Privacy Preserving Video Surveillance System. W F. Buccafurri, G. Semeraro (Editors), ECommerce and Web Technologies, vol. 61, pp. 73-84. Springer Berlin Heidelberg., 2010.
- [66] Lin, C.-F., Yuan, S.-M., Leu, M.-C., Tsai, C.-T.: A Framework for Scalable Cloud Video Recorder System in Surveillance Environment. 2012 9th International Conference on Ubiquitous Intelligence Computing and 9th International Conference on Autonomic Trusted Computing (UIC/ATC), pp. 655-660, 2012.
- [67] Xu, Z., Mei, L., Liu, Y., Hu, C., Chen, L.: Semantic enhanced cloud environment for surveillance data management using video structural description. Computing, 1-20, 2014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-43c6367c-2f23-4d35-ad26-e8a44c074429