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In this proposal, face detection algorithm is developed to detect the number of faces present in a vehicle and corresponding to the detection system gives the count of the people. The images are captured using the webcam, which is installed in a vehicle and connected through a raspberry pi model B. As the vehicle leaves the station, the camera captures the images of the passenger in a seating area. The system is based on real-time application the camera will continuously capture the images and the count is also continuously modified. Then images that are captured are pre-processed via improved and adjusted to reduce the noise using software application. After pre-processing and post-processing steps are performed the images is send to the server using websocket protocol. The system obtains the maximum number of passengers in vehicle using face detection technology and thus gives the total face count of the passengers.

Raspberry Pi, PI Camera, Haar Features, Contrast Limited Adaptive Histogram Equalization, SVM Classifier

[1] Janewit Wittayaprapakorn, Thongchai Yooyativong, “Vehicle Seat Vacancy Identification using Image Processing Technique,” IEEE publication, 2017.
[2] Milica G. Kisic1and Nelu V. Blaz, “Detection of seat occupancy using a wireless inductive sensor”, IEEE publication, 2015.
[3] Dwarakesh T P and S Ananda Subramaniam, “Vacant Seat Detection System using Adaboost and Camshift,” International Journal of Electrical and Computing Engineering Vol. 1, Issue. 3, April– 2015.
[4] Paul Viola and Michael Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” Accepted Conference on Computer Vision and Pattern Recognition 2001.
[5] SHU Chang, FANG Chi, “Histogram of the Oriented Gradient for Face Recognition,” TSINGHUA SCIENCE & TECHNOLOGY ISSN 1007-0214 15/15 pp216-224 Volume 16, Number 2, April 2011.
[6] Ravinder Kaur , Manvi and Anupama Gupta, “Vehicle’s Driver Face Recognition using Viola Jones and Support Vector Machine,” International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181,Vol. 6 Issue 01, January-2017.
[7] Ole Helvig Jensen, “Implementing the Viola-Jones Face Detection Algorithm,” Technical University of Denmark, ISSN 1601-233X, 2008.
[8] Himanshu Sharma and Sumeet Saurav, “Analyzing Impact of Image Scaling Algorithms on Viola-Jones Face Detection Framework,” IEEE publication, 2015.

G. S. Gawande, K. O. Gurbani, A. N. Dolas, "Design and Development of System for Identification of Vehicle Seat Vacancy," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.1-5, 2019.

Nowadays, there are huge amount of texts written in Amharic language. Theses texts, documents and related Amharic literatures are usable for individuals, who can read, hear and understand Only Amharic languages. But In Ethiopia, there are many individuals who cannot hear and understand any text and literature written in Amharic language unless there is parallel translation in language they are good. These documents need to be translated to Awngi to provide valuable information for Awngi language speakers. To conduct the research, the corpus was collected from Amharic texts, Mass Media Agency and Bible. We used minimum of 1500 simple sentences, 1000 compound and 1000 complex sentences and maximum of 5000 sentences for each sentences type in order to train the system. We used 9:1 ratio for training and testing respectively. For language model we used minimum of 5700 and maximum of 14491 monolingual sentence of Awngi language. To do the system, we used Moses for Mere Mortal for translation process, MGIZA++ for alignment and IRSTLM for language model. Experimental results showed that better performance of 37% BLUE score was registered using complex sentences. In Amharic language, a word in sentences can have more than one meaning. While translating, the challenge of this study was not translating the meaning of the given sentences according to the context. But this study has not solved that challenge which needs further study to show all meanings of word depending on the context properly

Awngi, Awngi translation, statistical, Amharic traslation,amharic-awngi translation

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[4]. Esubalew Asmare Desta. “Developing Awngi-Amharic cross Language information retrieval (Clir):A Dictionary Based Query Translation Approach.”M.Sc.thesis, University of Gondar, Ethiopia, 2015.
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[14]. Jabesa.” Bidirectional English-Afaan Oromo Machine Translation”. Msc. Thesis. Addis Ababa University, Ethiopia, 2013.
[15]. Nakul Sharma, English to Hindi Statistical Machine Translation System, MSC thesis, Thapar University Patiala, June 2011.
[16]. Mulu, Besacier.” English-Amharic Statistical Machine Translation”. PhD. Thesis. Addisababa University, Ethiopia.
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[19]. Melese Mihret.” Sentiment Analysis Model for Opinionated Awngi Text”.Msc. Thesis, University of Gondar. Ethiopia, 2017.
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[23]. Mulu Gebreegziabher, Laurent Besacier. “English – Amharic Machine Translation: An Experiment Using Statistical Approach.” PhD. Thesis, Addis Ababa University, Ethiopia.
[24]. John Sturdy DeNero. “Phrase Alignment Mo dels for Statistical Machine Translation”, PhD.thesis, University of California, Berkeley,2010.

Habtamu Mekonnen, "Amharic-Awngi Machine Translation: An Experiment Using Statistical Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.6-10, 2019.

Recruitment process has become one of the laying foundations for the development of an organization. All organizations are looking for the perfect candidate to build their enterprises. Finding the right candidate for the right job is becoming more and more difficult. Recruiter and other HR professionals that don`t use innovative recruiting strategies are often unable to find job candidates that are suitable for the job. To find the right candidates, recruiters have to have a well-planned and developed recruiting and hiring strategies. Machine learning is emerging as a strategy to help employers more efficiently conduct talent sourcing and recruitment. Traditional recruiting process requires lot of time and effort along with various costs that comes with it for filtering out the candidate. This paper will propose an automated interview system which uses machine learning to gauge the candidates based on the emotions expressed in the interview process and thus find the right person for the right job.

Machine Learning, Neural Network , Recruitment, Emotion, Speech

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Jiso K. Joy, Sreedev S.B. , Vishnu A.K., Rejimoan R., "An Intelligent Architecture for Recruitment Process Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.11-15, 2019.

Women security is a major concern and has become mandatory now-a-days. IT companies are looking forward to security solutions for women working at night shifts. This paper proposes a model that can be used to deal with the security issues of working women by tracking location, providing self-defense and alerting people. This proposed system also consists of a health monitoring system to monitor the heart rate and temperature of a person.

women, security, tracking, self-defense, health-monitoring

[1]. Piyush Kumar Verma, Arpit Sharma, Dhruv Varshney, Manish Zadoo “WOMEN SAFETY DEVICE WITH GPS, GSM AND HEALTH MONITORING SYSTEM” International Research Journal of Engineering and Technology (IRJET), Volume 5, Issue 3, March 2018.
[2]. Glenson Toney, Dr. Fathima Jabeen, Puneeth S “Design and Implementation of Safety Armband for Women and Children using ARM7” International Conference on Power and Advanced Control Engineering (ICPACE), 2015.
[3]. Sharifa Rania Mahmud, Jannutal Moawa, Ferry Wahyu Wibowo “Women Empowerment: One Stop Solution for Women” 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2017.
[4]. Madhura Mahajan, KTV Reddy, Manita Rajput “Design and Implementation of a Rescue System for Safety of Women” peer-reviewed and accepted to be presented at the IEEE WiSPNET Conference, 2016.
[5]. D. G. Monisha, M. Monisha, G. Pavithra and R. Subhashini “Women Safety Device and Application-FEMME” Article in Indian Journal of Science and Technology • March 2016.
[6]. Rashmi Deshmukh, Anagha Zade, Rasika Bhusari, “Temperature Monitoring and Regulating System for Power Saving”International Journal of Scientific Research in Computer Science and Engineering, Volume-1, Issue-2, March-April-2013
[7]. Harmeet Khanuja , Samruddhi Kalekar, Prasad Narode, Sanket Sanap , Dnyaneshwar Giri, “IOT Based Smart Parking System”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.44-49, Feb 2018
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[11]. Harshitha M S , Chaithra P R ,Chaithra S , Akshatha kamath , “GPS and GSM Based Self Defence System for Women Safety”, International Journal of Innovative Research in Science, Engineering and Technology Volume 7, Special Issue 6, May 2018

R Ujwala, Poojashree H M, Rachana Y V, Vandana K V, Subodh Kumar Panda, "Design and Implementation of Safety and Health Monitoring System for Women," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.16-21, 2019.

Nowadays a huge amount of multimedia contents is generated in disparate manners with different devices and then uploaded on the Internet. During upload or once on-line, they are shared with other known users and, ultimately, played or downloaded. These digital assets, accessible on the Internet, mostly flow through social networks (SN) and constitute a real-time source of information. Filter has been performer an image possibly will be of elemental import to go back to its provenance. In this Project it is such a context and proposes an innovative method to inquire if an image derives from a social network. The modus operandi is based on the assumption that each social network applies a peculiar and mostly unknown strategy that however leaves some distinct traces on the image such traces can be extract to feature every dais. By resorting at trained classifiers, the presented methodology is satisfactorily able to discern different social network origin. This method is also able to go back to the original JPEG quality factor the image had before being uploaded on a social network.

Social Network(SN), Multimedia,Classifier,Image Quality

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C. Muruganandam, Pushpavalli, N. Ruba, "Multiple Parenting Phylogeny Relationships in Digital Images," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.22-26, 2019.

Data mining techniques can be used by Health organizations to predict different types of Cancer disease using individual Gene expression data. By using DNA (Deoxyribo Nucleic Acid) Microarray technology, thousands of genes can be articulated simultaneously. The objective of this research is to look closer on the classification issues in handling microarray data by introducing Semi-Supervised KNN (K-Nearest Neighbor) algorithm and Particle Swarm Optimization (PSO) as feature selection to cluster large amount of genetic microarray data. Also, using the predicted type of cancer, the severity level of cancer is diagnosed. Classifier performance is evaluated and it is shown in pie-chart and graph with improved accuracy. The proposed Semi-supervised learning method provides 10% improved accuracy in predicting cancer than the existing Supervised and unsupervised learning methods.

Medical Data Mining, Cancer Prediction, Gene sequence, Clustering, Classification

[1] B. Jaison, G. Chilambuchelvan, K. Nirmal, “A hybrid approach for gene selection and classification using support vector machine”, International Arab Journal Information Technology, Vol.12, issue.6A, pp.695-700, 2015.
[2] P. M. Booma , S. Prabhakaran, “Classification of genes for disease identification using data mining techniques”,Journal of Theoretical and Applied Information Technology, vol.83, issue.3, pp.399-414, 2016.
[3] L. George, S. Asha ,W. Haibo , D.F. Michael , R.M. Stephen , N.C.S. Natalie , S. Elaine, R. Timothy, E.T. John, M. Anant, “Supervised Multi-view Canonical Correlation Analysis (sMVCCA): Integrating Histologic and proteomic features for predicting Recurrent prostate cancer”, IEEE transactions on Medical Imaging,vol.34,issue.1, pp.284-297,2015.
[4] A. Hasseeb, H. Jingyu, X. Yong, A. Russul, “ Lung Cancer Prediction from Microarray data by Gene expression programming”, IET Systems Biology,vol.10,issue.5, pp.168-178, 2016.
[5] Liu, Jin-Xing, Yong Xu, Chun-Hou Zheng, Heng Kong, Zhi-Hui Lai , “RPCA-Based Tumor Classification Using Gene Expression Data”,IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) ,vol.12,issue.4, pp.964-970, 2015.
[6] R. Nagpal, R. Shrivas, “Cancer Classification Using Elitism PSO Based Lezy IBK on Gene Expression Data”, International Journal of Scientific and Technical Advancements, vol.1, issue.4, pp.19-23, 2015.
[7] A. Natarajan, R. Bala Subramanian , “A Fuzzy Parallel Island Model Multi Objective Genetic Algorithm Gene Feature Selection for Microarray Classification”, International Journal of Applied Engineering Research, Vol.11, issue.4, pp.2761-2770, 2016.
[8] H. Park, Y. Shiraishi, S. Imoto, S. Miyano, “A novel Adaptive Penalized Logistic Regression for uncovering biomarker associated with Anti-cancer drug sensitivity”, IEEE/ACM transactions on Computational Biology and Bioinformatics, vol.14, issue.4, pp.771-782, 2017.
[9] N. Songyot, “Gene selection using interaction information for Microarray-based Cancer classification”, IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2016.
[10] P. Thangaraju , R. Mehala, “Novel Classification Based Approaches over Cancer Diseases”, International Journal of Advanced Research in Computer and Communication Engineering, vol.4, issue.3, pp.294-297, 2015.
[11] R. Anupriya, P. Saranya , R. Deepika , “Mining Health Data in Multimodal Data Series for Disease Prediction”, International Journal of Scientific Research in Computer Science and Engineering ,Vol.6, Issue.2, pp. 96-99, 2018.
[12] Pramod Pardeshi and Ujwala Patil , “Fuzzy Association Rule Mining- A Survey”,International Journal of Scientific Research in Computer Science and Engineering ,Vol.5, Issue.6, pp.13-18, 2017.

T. Sneka, K. Palanivel, "Pattern Similarity Based Classification Using K-Nearest Neighbor and PSO Model for Cancer Prediction with Genetic Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.27-31, 2019.

The cluster head selection of any clustering technique in Wireless sensor networks effects stability of a network and also makes that particular protocol more efficient. Many cluster head techniques in any cluster protocol like MODLEACH is based on probability and threshold but does not affect the network life if used in multihop concept. This paper, presents an energy efficient multi-hop of clustering approach in MOLEACH, where cluster heads are selected on the basis of remaining energy of sensor nodes and probability parameters. The node having the maximum energy will get more chance to act as a Cluster head. It increases the stability period of the network because cluster head needs more energy than normal nodes.

WSN, MODLEACH, cluster heads, Residual energy

[1] Manpreet Saini*1, Sukhbeer Singh2, Neelam Chouhan 3 ,” Survey On Multihop Cluster Head Techniques in MODLEACH “, IJCSE, Vol.-6, Issue-9, May 2019
[2] Pyush shaarma et al, “Enhancing MODLEACH using Multihop Cluster Heads as Forwarder Nodes” ijirs, Volume – 7 Issue -2 February 2018.
[3] Priyanka et al, “Enhanced MODLEACH Using Effective Energy Utilization Technique for Wireless Sensor Network “,International Journal Of Engineering And Computer Science”, Volume 5 Issue 09 September 2016 .
[4] Mr. Santosh.Irappa.Shirol et al, “Advanced-LEACH Protocol of Wireless Sensor Network”, International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013.
[5] Rajashree.V.Biradar Et Al, “Classification And Comparison Of Routing Protocols In Wireless Sensor Networks”, UbiCC Journal – Volume 4.
[6] Chunyao FU et al, ” An Energy Balanced Algorithm of LEACH Protocol in WSN”, International Journal of Computer Science Issues, Vol. 10, Issue 1, No 1, January 2013.
[7] I.F. Akyildiz, W. Su*, Y. Sankarasubramaniam, E. Cayirci,” Wireless sensor networks: a survey”, Elsevier, Computer Networks 38, 393–422, 2002.
[8] S. Rani and S.H. Ahmed, Multi-hop Routing in Wireless Sensor Networks, Springer Briefs in Electrical and Computer Engineering.
[9] Jing, Yang, Li Zetao, and Lin Yi. "An improved routing algorithm based on LEACH for wireless sensor networks." Control and Decision Conference (CCDC), 25th Chinese. IEEE, 2013.
[10] Beiranvand, Zahra, Ahmad Patooghy, and Mahdi Fazeli. "I-LEACH: An efficient routing algorithm to improve performance & to reduce energy consumption in Wireless Sensor Networks." Information and Knowledge Technology (IKT), 5th Conference on. IEEE, 2013.
[11] Xu, Jia, Ning Jin, Xizhong Lou, Ting Peng, Qian Zhou, and Yanmin Chen. "Improvement of LEACH protocol for WSN.", In Fuzzy Systems and Knowledge Discovery (FSKD), IEEE 9th International Conference on, pp. 2174-2177, 2012.

Manpreet Saini, Sukhbeer Singh, Neelam Chouhan, "Improved Modleach By Using More Energetic Cluster Head Selection Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.32-35, 2019.

The energy conservation in a network is a key challenge for a protocol, which affects the network for better data communication and scheduling. This paper presents a model which works in levels and in each level. The data distribution and collection in each level are done by CHs. This proposed work will focus on distance based data transmission and load dividing techniques at level 1 between CHs and normal node used as new node concept used as CH node. Which will help to stabilize the network by maximizing the lifespan of the network. The objectives of the work is to implement reclustering, multihop data transmission processes model and data distribution model by minimizing the packet loss accomplishing the load divide technique at level 1 among nodes.

Wireless Sensor Network, reclustering, cluster head, LEACH

[1] Navneet Kaur1*, Navjeet Saini2, Sandeep Kour3 ,” The Survey On Data Transfer techniques in Wireless Body Sensor Networks”, International Journal of Computer Sciences and Engineering, Volume-6, Issue-9, 2019
[2] Sonia 1*, Deepak Kumar2,” Load Dividing and Reclustering technique to Improve the Reliability of Data In a Network”, International Journal of Computer Sciences and Engineering, Volume-5, Issue-8, 2019.
[3] 1Jaswant Singh Raghuwanshi,2Neelesh Gupta,3Neetu Sharma, “energy fficient data communication approach in wireless sensor networks”, International Journal of Advanced Smart Sensor Network Systems (IJASSN), Vol 4, No.3, July 2014.
[4] Ashim Kumar Ghosh1, Anupam Kumar Bairagi2, Dr. M. Abul Kashem3, Md. Rezwan-ul-Islam1, A J M Asraf Uddin1, “ Energy Efficient Zone Division Multihop Hierarchical Clustering Algorithm for Load Balancing in Wireless Sensor Network”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 12, December 2011.
[5] I.F. Akyildiz, W. Su*, Y. Sankarasubramaniam, E. Cayirci,” Wireless sensor networks: a survey”, Elsevier, Computer Networks 38, 393–422, 2002.
[6] M. Shanmukhi, 2G. Nagasatish, “LOAD BALANCING USING CLUSTERING IN WSN WITH FUZZY LOGIC TECHNIQUES”, International Journal of Pure and Applied Mathematics Volume 119 No. 14 2018, 61-69.
[7] Shankar Sachdev1, Laxman Yalmar2, Nilesh Gaykhe3, “ Energy Efficient Cluster Based Routing Algorithm in Wireless Sensor Networks”, IJESC, vol. 6 issue 3, 2016.
[8] Jing, Yang, Li Zetao, and Lin Yi. "An improved routing algorithm based on LEACH for wireless sensor networks." Control and Decision Conference (CCDC), 2013 25th Chinese.IEEE, 2013.
[9] Beiranvand, Zahra, Ahmad Patooghy, and Mahdi Fazeli. "I-LEACH: An efficient routing algorithm to improve performance & to reduce energy consumption in Wireless Sensor Networks." Information and Knowledge Technology (IKT), 2013 5th Conference on. IEEE, 2013.
[10] Liu, Yi, Shan Zhong, Licai You, Bu Lv, and Lin Du. "A Low Energy Uneven Cluster Protocol Design for Wireless Sensor Network." Int`l J. of Communications, Network and System Sciences 5 (2012): 86.
[11] Haneef, Muhammad, Zhou Wenxun, and Zhongliang Deng. "MG-LEACH: Multi group based LEACH an energy efficient routing algorithm for Wireless Sensor Network." Advanced Communication Technology (ICACT), 2012 14th International Conference on. IEEE, 2012.
[12] Aslam, M., Nadeem Javaid, A. Rahim, U. Nazir, Ayesha Bibi, and Z. A. Khan. "Survey of extended LEACH-Based clustering routing protocols for wireless sensor networks."In High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on, pp. 1232-1238. IEEE, 2012.
[13] Mr. Santosh.Irappa.Shirol, Ashok Kumar. N, Mr. Kalmesh.M.Waderhatti,”Advanced-LEACH Protocol of Wireless Sensor network”, IJETT - Volume4 Issue6- June 2013.

Navneet Kaur, Navjeet Saini, Sandeep Kaur, "Enhancement of Levelwise Cluster Head Selection with the Concept of New Nodes As Cluster Head Nodes," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.36-38, 2019.

Cloud computing is an increasingly famous and growing technology which has led to a new dawn in the field of Information Technology. It has created a drastic change in the trend of different digital devices. Cloud Computing corresponds to both, the applications provided as services over the internet and the hardware elements and systems software in the data-centers that provide those respective services. In this paper, improving the security of data within the cloud when the data is migrated from one source to cloud or vice-versa, using an enhanced randomized encryption technique. During the analysis of providing security to a large amount of data in the cloud environment using various encryption techniques, we formulated that the asymmetric algorithms are incapable to encrypt a data in a bulk or in large amount when used singly. Security is the major issue in cloud computing system so we are using the concept of asymmetric algorithm when we are migrating one source file to the cloud.

Cloud computing, Software as a services, Encryption, Asymmetric algorithms, RSA, AES

[1] Secure Migration of Various Databases over A Cross Platform Environment, an International Journal of Engineering and Computer Science ISSN: 2319-7242 Volume 2 Issue 4 April, 2013.
[2] Understanding pricing and migration cost for Cloud adoption in business environments by Dimitris Monogenis, MSc Computing and Management 2011/2012.
[3] Mobile One Time Passwords and RC4 Encryption for Cloud Computing , Master’s Thesis in Computer Network Engineering by Markus Johnsson & A.S.M Faruque Azam.
[4] 15th International Conference on Management of Data COMAD 2009, Mysore, India, December 9–12, 2009 ©Computer Society of India, 2009, A Unified and Scalable Data Migration Service for the Cloud Environments.
[5] Data Migration: Connecting Databases in the Cloud, a research paper published by authors: Farah Habib Chanchary and Samiul Islam in ICCIT 2012.
[6] Using the cloud for data migration: practical issues and legal implications - 16 Feb 2011 - Computing Feature.
[7] Database security in the cloud by Imal Sakhi, Examensarbete inom Datateknik Grundnivå, 15 hp Stockholm 2012.
[8] A Symmetric Key Cryptographic Algorithm by Ayushi, ©2010 International Journal of Computer Applications (0975 - 8887) Volume 1 – No. 15
[9] Microsoft Data Encryption Toolkit for Mobile PCs: Security Analysis Version 1.0, published: April 2007.
[10] “A Security approach for Data Migration in Cloud Computing”, an International Journal of Scientific and Research Publications, Volume 3, Issue 5, May 2013 1 ISSN 2250-3153.
[11] “Cloud computing a CRM Service Based on Separate Encryption and Decryption using Blowfish algorithm”, International Journal on Recent and Innovation Trends in Computing and Communication Volume: 1 Issue: 4 217 – 223
[12] Slim Trabelsi, Yves Roudier. , Research Report RR-06-164 Enabling Secure Service Discovery with Attribute Based Encryption.
[13] RSA algorithm achievement with federal information processing signature for data protection in cloud computing, International Journal of Computers & Technology, ISSN: 2277-3061 Volume 3. No. 1, AUG, 2012.
[14] International Journal of Scientific Research in Computer Sciences and Engineering (ISSN: 2320-7639)
[15] International Journal of Scientific Research in Network Security and Communication (ISSN: 2321-3256)

Akanksha Aasarmya, Sohit Agarwal, "Improving Security for Data Migration in Cloud Computing using Randomized Encryption Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.39-43, 2019.

Vehicle area n/w (VANET) has been come a long distance since its inception. After smart cities and smart village, smart roads are required to manage the traffic effectively and efficiently. VANET recognize a vehicle and trace it. Establishing connection and serving the request come once a vehicle is recognized appropriately and trekked it serves a great help in video surveillance of moving objects too. Purpose of surveillance but recognizing them in a difficult environment is always a challenge the proposed work detects single moving vehicles and multiple moving vehicles under dense environment like foggy condition. The frames are read as images, noise is filtered on two Averaging and Median filter. An improvised Gaussian mixture model on two dimensional structural elements has been proposed in the thesis. The results obtained are compared with standard optical flow algorithm to detect moving vehicles; the proposed algorithm improves false alarm rate, precision, accuracy, occlusion rate. It concludes that the proposed algorithm works better than existing optical flow algorithm for single and multiple vehicle detection in a dense environment.

object detection, precision, occlusion rate, accuracy, false alarm

[1] Habib Mohammed Hussien et al., Moving Object detection and tracking International Journal of Engineering and Technical Research, 2014, vol 3,10,2278-0181
[2] Qiang Chen, Quan-Sen Sun, two-stage object tracking method and a contourbased method IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2010, VOL. 20, NO. 4,
[3] Ozcanli Ozbay,ozge ccan, recognization of vehicle ., International Journal of Science and Research (IJSR) ISSN (Online): 2010, 2319-7064
[4] Rogerio Schmidt Feris, Large-Scale Vehicle Detection, Indexing,and Search in Urban Surveillance Videos, IEEE TRANSACTIONS ON MULTIMEDIA, 2012,VOL. 14, NO. 1
[5] Tianzhu Zhang, Si Liu, visual surveillance systems .IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2013, VOL. 9, NO. 1,
[6] Rupali S.Rakibe*, Bharati D.Patil May 2013,Background Subtraction algorithm based human tracking, International Journal of Scientific and Research Publications, 2013, Volume 3, Issue 5 1 ISSN 2250-3153
[7] Jamal Raiyn, Video surveillance system Advances in Internet of Things, 2013, 3, 73-78
[8] Priyanka Gokarnkar #1 and Clitus Neil D’souza *2, moving object detection International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) 2015, ISSN: 0976-1353 Volume 14 Issue 2
[9] K. Kalirajan1 andM. Sudha2, detect and track the moving target in compressed video domain Hindawi Publishing Corporation the Scientific World Journal Volume 2015, Article ID 907469,
[10] Nidhi, Image Processing and Object Detection, International Journal of Applied Research 2015; 1(9): 396-399
[11] Roxana Velazquez-Pupo 1, Alberto Sierra-Romero 1 , performance vision-based system with a single static camera, Sensors, 2018, 18, 374
[12] Pawan Kumar Mishra and GP Saroha , detection and tracking for moving objects using feature extraction , International journal of engineering and future technology , 2018,15,2:2455-6432

Kanchan Godiyal, Pawan Kumar Mishra, "Vehicle Detection in Denser Environment Using Gaussian Model," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.44-48, 2019.