Abstract
Detection of abnormal trajectories in a traffic scene is an important problem in Video Traffic Surveillance (VTS). Recently, General Potential Data Field (GPDf)-based trajectory clustering scheme has been adopted for detecting abnormal events such as illegal U-turn, wrong side and unusual driving behaviors and it uses spatial and temporal attributes explicitly. The concept of data field is used to discover the relation between the spatial points in data-space and grouping them into clusters based on their mutual interaction. Existing methodologies related to potential data field-based clustering have certain limitations such as pre-defined cluster size, non-effective cluster center identification, and limitation in range estimation using isotropic impact factor (h) which leads to inaccurate results. In order to address the above-mentioned issues, this paper proposes an efficient anomaly detection scheme based on General Potential Data field with Spectral Clustering (GPDfSC). The proposed GPDfSC scheme utilizes potential data field technique along with spectral clustering for effective identification of abnormalities. The Limitation in impact factor(h) is overcome by using anisotropic impact parameter Bmat. Further, Bayesian Decision theory is used to classify the events as normal or abnormal. The proposed scheme is implemented in real time using GPU and from the results it is found that it gives 12% better accuracy in detecting abnormalities than the state of art technique.
Similar content being viewed by others
References
Ashok Kumar PM, Vaidehi V (2015) Anomalous event detection in traffic video based on sequential temporal patterns of spatial interval events. KSII Trans Internet Inform Syst 9(1):169–189. https://doi.org/10.3837/tiis.2015.01.010.
Batapati P, Tran D, Sheng W, Liu M, Zeng R (2014) Video analysis for traffic anomaly detection using support vector machines. 1th World Congress on Intelligent Control and Automation: 5500–5505. doi:https://doi.org/10.1109/WCICA.2014.7053655
Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. KDD workshop 10(16):359–370
Bhattacharyya A (1943) On a measure of divergence between two statistical populations defined by their probability distributions. Bull Calcutta Math Soc 35:99–109
Blatt FJ (1986) Principles of physics (2nd ed.). published: Allyn and Bacon, Boston
Cai Y, Wang H, Chen X, Jiang H (2015) Trajectory-based anomalous behavior detection for intelligent traffic surveillance. IET Intell Transp Syst 9(8):810–816. https://doi.org/10.1049/iet-its.2014.0238
Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM computing surveys (CSUR) 41(3):1–15. https://doi.org/10.1145/1541880.1541882
Cyert RM, DeGroot MH (1987) Bayesian decision theory. In: Bayesian analysis and uncertainty in economic theory. Springer, Dordrecht
Determine the optimal number of cluster. http://www.sthda.com/english/wiki/print.php?id=239. Accessed 26 October 2017
Djalalov M, Nisar H, Salih Y, Malik AS (2010) An algorithm for vehicle detection and tracking. In Intelligent and Advanced Systems (ICIAS): 1–5. doi:https://doi.org/10.1109/ICIAS.2010.5716189
Douglas DH, Peucker TK (1973) Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: Int J Geograph Inform Geo Visual 10(2):112–122. https://doi.org/10.3138/FM57-6770-U75U-7727
Fang M, Wang S, Jin H (2010) Spatial neighborhood clustering based on data field. Int Conf Adv Data Mining Applic 6440:262–269. https://doi.org/10.1007/978-3-642-17316-5_25
Fu Z, Hu W, Tan T (2005) Similarity based vehicle trajectory clustering and anomaly detection. IEEE Int Conf Image Process 2:602–605. https://doi.org/10.1109/ICIP.2005.1530127.
Horova I, Kolacek J, Vopatova K (2013) Full bandwidth matrix selectors for gradient kernel density estimate. Comput Stat Data Anal 57(1):364–376. https://doi.org/10.1016/j.csda.2012.07.006
Hu W, Tan T, Wang L, Maybank S (2004) A survey on visual surveillance of object motion and behaviors. IEEE Trans Syst Man Cybern Part C Appl Rev 34(3):334–352. https://doi.org/10.1109/TSMCC.2004.829274
Hung MH, Pan JS, Hsieh CH (2010) Speed up temporal median filter for background subtraction. 2010 First International Conference on In Pervasive Computing Signal Processing and Applications (PCSPA), 297–300. IEEE
iLIDS datasets. http://www.eecs.qmul.ac.uk/~andrea/avss2007_d.html. Accessed on May 2017/
Jiang F, Wu Y, Katsaggelos AK (2009) A dynamic hierarchical clustering method for trajectory-based unusual video event detection. IEEE Trans Image Process 18(4):907–913. https://doi.org/10.1109/TIP.2008.2012070
Jiang F, Yuan J, Tsaftaris SA, Katsaggelos AK (2011) Anomalous video event detection using spatiotemporal context. Comput Vis Image Underst 115(3):323–333. https://doi.org/10.1016/j.cviu.2010.10.008
Jung CR, Hennemann L, Musse SR (2008) Event detection using trajectory clustering and 4-D histograms. IEEE Trans Circuits Syst Video Technol 18(11):1565–1575. https://doi.org/10.1109/TCSVT.2008.2005600
Kumar PA, Vaidehi V (2017) A transfer learning framework for traffic video using neuro-fuzzy approach. Sadhana 42(9):1431–1442. https://doi.org/10.1007/s12046-017-0705-x
Kumar PA, Sathya V, Vaidehi V (2015) Traffic rule violation detection in traffic video surveillance. Int J Comput Sci Electron Eng (IJCSEE) 3(4):301–307
Laxhammar R, Falkman G (2014) Online learning and sequential anomaly detection in trajectories. IEEE Trans Pattern Anal Mach Intell 36(6):1158–1173. https://doi.org/10.1109/TPAMI.2013.172
Li D, Du Y (2007) Artificial intelligence with uncertainty. Chapman and Hall/CRC 193–211
Li D, Wang S, Gan W, Li D (2011) Data field for hierarchical clustering. Int J Data Warehouse Min 7(4):43–63. https://doi.org/10.4018/jdwm.2011100103
Liu YH, Chen WQ (2011) Line simplification algorithm implementation and error analysis. IEEE International Conference on Computer Science and Automation Engineering 64–68. doi:https://doi.org/10.1109/CSAE.2011.5952424
Liu Y, Jin J, Zhang Y, Xu C (2014) A new clustering algorithm based on data field in complex networks. J Supercomput 67(3):723–737. https://doi.org/10.1007/s11227-013-0984-x
Meng H-D, Wu P-F, Song Y-C, Xu G-D (2013) Research of clustering algorithm based on different data field model. Trans Tech Public 760-762:1925–1929. https://doi.org/10.4028/www.scientific.net/AMR.760-762.1925
Morris BT, Trivedi MM (2011) Trajectory learning for activity understanding: unsupervised, multilevel, and long-term adaptive approach. IEEE Trans Pattern Anal Mach Intell 33(11):2287–2301. https://doi.org/10.1109/TPAMI.2011.64
Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: analysis and an algorithm. Adv Neural Inf Proces Syst: 849–856.
NGSHIM datasets. https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj. Accessed on November 2018
Ranjith R, Athanesious JJ, Vaidehi V (2015) Anomaly detection using DBSCAN clustering technique for traffic video surveillance. In Advanced Computing (ICoAC), Seventh International Conference.1–6. IEEE. doi: https://doi.org/10.1109/ICoAC.2015.7562795
Sheather SJ (2004) Density estimation. Instit Math Stat 19(4):588–597. https://doi.org/10.1214/088342304000000297
Wan Y, Yang TI, Keathly D, Buckles B (2014) Dynamic scene modelling and anomaly detection based on trajectory analysis. IET Intell Transp Syst 8(6):526–533. https://doi.org/10.1049/iet-its.2012.0119
Wand MP, Jones MC (1995) Kernel smoothing. Number 60 in monographs on statistics and applied probability. Chapman & Hall/CRC
Wang X, Ma KT, Ng GW, Grimson WE (2011) Trajectory analysis and semantic region modeling using nonparametric hierarchical bayesian models. Int J Comput Vis1;95(3):287–312. doi:https://doi.org/10.1007/s11263-011-0459-6.
Wang S, Li Y, Wang D (2016) Data field for mining big data. Geo-spatial Inform Sci 19(2):106–118. https://doi.org/10.1080/10095020.2016.1179896
Wang S, Wang S, Yuan H, Li Q, Geng J, Yu Y (2018) Clustering by differencing potential of data field. Computing 100(4):403–419. https://doi.org/10.1007/s00607-018-0605-x
Zhang T, Liu S, Xu C, Lu H (2013) Mining semantic context information for intelligent video surveillance of traffic scenes. IEEE Trans Indust Inform 9(1):149–160. https://doi.org/10.1109/TII.2012.2218251
Zhao P, Qin K, Ye X, Wang Y, Chen Y (2017) A trajectory clustering approach based on decision graph and data field for detecting hotspots. Int J Geogr Inf Sci 3(6):1101–1127. https://doi.org/10.1007/978-3-642-17316-5_25
Zhou Y, Yan S, Huang TS (2007) Detecting anomaly in videos from trajectory similarity analysis. Multimedia and Expo IEEE International Conference 1087–1090. doi:https://doi.org/10.1109/ICME.2007.4284843
Acknowledgements
The first author extends his sincere gratitude to Anna university for supporting this research through Anna Centenary Research Fellowship (ACRF) and also grateful to Dr. R. Balasubramanian and Dr. Partha Pratim Roy from IIT-Roorkee for their valuable inputs and suggestions.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Athanesious, J.J., Chakkaravarthy, S.S., Vasuhi, S. et al. Trajectory based abnormal event detection in video traffic surveillance using general potential data field with spectral clustering. Multimed Tools Appl 78, 19877–19903 (2019). https://doi.org/10.1007/s11042-019-7332-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-019-7332-y