Statistical accuracy analysis of different detecting algorithms for surveillance system in smart city

Hassan Al-Yassin, Jaafar I. Mousa, Mohammed A. Fadhel, Omran Al-Shamma, Laith Alzubaidi


Several detecting algorithms are developed for real-time surveillance systems in the smart cities. The most popular algorithms due to its accuracy are: Temporal Differencing, Background Subtraction, and Gaussian Mixture Models. Selecting of which algorithm is the best to be used, based on accuracy, is a good choise, but is not the best. Statistical accuracy anlysis tests are required for achieving a confident decision. This paper presents further analysis of the accuracy by employing four parameters: false recognition, unrecognized, true recognition, and total fragmentation ratios. The results proof that no algorithm is selected as the perfect or suitable for all applications based on the total fragmentation ratio, whereas both false recognition ratio and unrecognized ratio parameters have a significant impact. The mlti-way Analysis of Variate (so-called K-way ANONVA) is used for proofing the results based on SPSS statistics.


Background subtraction; Gaussian mixture models; K-way ANOVA; SPSS; Temporal differencing

Full Text:



Carlos Cuevas, and NarcisoGarcía, "Improved Background Modeling For Real-time Spatio-temporal Non-parametric Moving Object Detection Strategies", Elsevier, Image and Vision Computing, 31, pag. 616-630, 2013.

Pritam Das, RanjitGhoshal, Dipak Kumar Kole, and Rabindranath Ghosh,"Measurement of Displacement and Velocity of a Moving Object from Real Time Video", International Journal of Computer Applications (0975 – 8887) Volume 49– No.13, July 2012.

Dashan Gao and Jie Zhou, "Adaptive Background Estimation for Real-time Traffic Monitoring", IEEE Intelligent Transportation Systems Conference Proceedings - Oakland , USA - August 25-29, 2001.

Deng, Jianxin. "Architecture design of the vehicle tracking system based on RFID." TELKOMNIKA Indonesian Journal of Electrical Engineering 11.6 (2013): 2997-3004.

Humaidi, Amjad J., Mohammed Abdulraheem Fadhel, and Ahmed R. Ajel. "Lane detection system for day vision using altera DE2." TELKOMNIKA 17.1 (2019): 349-361.

PlamenAngelov, RaminRamezani and Xiaowei Zhou, Student Member, "Autonomous Novelty Detection and Object Tracking in Video Streams using Evolving Clustering and Takagi-Sugeno Type Neuro-Fuzzy System", IEEE, International Joint Conference on Neural Networks 2008.

G. Prabhakar and B. Ramasubramanian, "An Efficient Approach for Real Time Tracking of Intruder and Abandoned Object in Video Surveillance System", International Journal of Computer Applications (0975 – 8887) Volume 54– No.17, September 2012.

Kamal Sehairi, Fatima Chouireb, Jean Meunier, “Comparative study of motion detection methods for video surveillance systems,” J. Electron. Imaging 26(2), 023025 doi: 10.1117/1.JEI.26.2.02302 , 2017.

Isabel Martins, Pedro Carvalho, Luís Corte- RealJosé& Luis Alba-Castro, “BMOG: Boosted Gaussian Mixture Model with Controlled Complexity,” Iberian Conference on Pattern Recognition and Image Analysis,vol.547, No 2, pp:( 50-57), August 2017.

Henry Schneiderman and Takeo Kanade, "A Statistical Method for 3D Object Detection Applied to Faces and Cars", IEEE, 2001.

R. Cucchiara, P. Onfiani, A. Prati, N. Scarabottolo, " Segmentation of Moving Objects at Frame Rate: A Dedicated Hardware Solution ", Proceedings of 7th IEE Conference on Image Processing and its Applications, 1999.

Fadhel, Mohammed Abdulraheem, Omran Al-Shamaa, and Bahaa Husain Taher. "Real-Time detection and tracking moving vehicles for video surveillance systems using FPGA." International Journal of Engineering & Technology 7.2.31 (2018): 117-121.

Hassan, Inaam Rikan and Mohammed Abdulraheem Fadhel. “Tracking Vehicles in Urban Smart City Based Xilinx Platform.”, Journal of Theoretical and Applied Information Technology, (2019).

Albiol, Antonio, Maria Julia Silla, Alberto Albiol, and Jose Manuel Mossi. "Video analysis using corner motion statistics." In IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp. 31-38. 2009

Liu, Chang, Guijin Wang, Wenxin Ning, Xinggang Lin, Liang Li, and Zhou Liu. "Anomaly detection in surveillance video using motion direction statistics." In 2010 IEEE International Conference on Image Processing, pp. 717-720. IEEE, 2010.

Zhou, Wenjun, Shun'ichi Kaneko, Manabu Hashimoto, Yutaka Satoh, and Dong Liang. "Co-occurrence Background Model with Hypothesis on Degradation Modification for Robust Object Detection." In VISIGRAPP (5: VISAPP), pp. 266-273. 2018.

Slawomir Bak, Marco San Biagio, Ratnesh Kumar, Vittorio Murino, François Bremond. Exploiting Feature Correlations by Brownian Statistics for People Detection and Recognition. IEEE transactions on systems, man, and cybernetics, Institute of Electrical and Electronics Engineers (IEEE), 2016. (hal-01850064)

Warne, Russell T. "A Primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists." Practical Assessment, Research & Evaluation 19 (2014).

Konietschke, Frank, et al. "Parametric and nonparametric bootstrap methods for general MANOVA." Journal of Multivariate Analysis 140 (2015): 291-301.

Engel, J., et al. "Regularized MANOVA (rMANOVA) in untargeted metabolomics." Analytica chimica acta 899 (2015): 1-12.

Asiltürk, Ilhan, Süleyman Neşeli, and Mehmet Alper Ince. "Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods." Measurement 78 (2016): 120-128.

Mahamuni P.D, R.P Patil and H.S.Thakar, “Moving Object Detection Using Background Subtraction Algorithm using simulink” in IJERT Volume 3, Issue 6, 2014.

Asim R. Aldhheri and Eran A. Edirisinghe, “Detection and classification of a moving object in a video stream” in ACIT 2014.

Santoyo-Morales, Juana E., and Rogelio Hasimoto-Beltran. "Video background subtraction in complex environments." Journal of applied research and technology 12.3 (2014): 527-537.

Cardinal, Rudolf N., and Michael RF Aitken. “ANOVA for the behavioral sciences researcher”, Psychology Press, 2013.

Boisgontier, Matthieu P., and Boris Cheval. "The anova to mixed model transition." Neuroscience & Biobehavioral Reviews 68 (2016): 1004-1005.

Chatfield, Chris,” Introduction to multivariate analysis” Routledge, 2018.

Total views : 174 times


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics