SMART POLICE TRACKING USING ANDROID PHONE K

SMART POLICE TRACKING USING ANDROID PHONE
K.Reventh1
Student of Final year, IFET College of Engineering,
Villupuram. 605108.

[email protected]

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Ms.Nivetha Kumari2,
Assistant Professor, IFET College of Engineering,
Villupuram. 605108.

[email protected]
9677354556.

Abstract-The majority of positioning systems have been designed to operate within environments that have a long-term stable macro-structure with potential small-scale dynamics. A smart positioning system for tracking policeman in highly dynamic industrial environments, such as secret missions by using Android Application. We show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy.

INTRODUCTION
The majority of positioning systems have been designed to operate within environments that have a long-term stable macro-structure with potential small-scale dynamics. A novel positioning system for tracking policeman in secret missions by using Android Application. It shows how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy.
The positioning systems have been designed to operate within environments that have a long-term stable macro-structure with potential small-scale dynamics. It is very difficult to predict the person who is beside us. Sometime mistakes may occur due to misunderstanding between their team met. An accident is occurred in police mission. A policeman shot another policeman who is in their same team due to insufficient tracking and misunderstanding between them. To track the people in secret mission this mobile app is very useful in police department.

1.1 SCOPE OF THE PROJECT
To show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy. We have conducted extensive real-world experiments in a team and how they show their coordination to complete their task.

2. LITERATURE SURVEY
2.1.1. Device-Free Human Localization Using Panoramic Camera and Indoor Map
The widespread application of camera-based surveillance systems has inspired extensive investigation on human localization. A device-free localization method using panoramic camera and indoor map. After pre processing the images observed with a panoramic camera, we detect human object as foreground using the widely used background subtraction method. Then we search all the foreground pixels and find the pixel whose location can represents the object’s location best.
2.1.2. Tracking People in Highly Dynamic Industrial Environments
Existing CCTV camera infrastructure found in many industrial settings along with radio and inertial sensors within each worker’s mobile phone to accurately track multiple people. This multi-target multi-sensor tracking framework also allows our system to use cross-modality training in order to deal with the environment dynamics. In particular, we show how our system uses cross-modality training in order to automatically keep track environmental changes (i.e. new walls) by utilizing occlusion maps. In addition, we show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy. We have conducted extensive real-world experiments in a construction site showing significant accuracy improvement via cross-modality training and the use of social forces.

2.1.3. Localization in highly dynamic environments using dual-timescale
NDT-MCL.

Dual-Timescale Normal Distributions Transform Monte Carlo Localization (DTNDT-MCL) – is a particle filter based localization method, which simultaneously keeps track of the pose using an apriori known static map and a short-term map. The short-term map is continuously updated and uses Normal Distributions Transform Occupancy maps to maintain the current state of the environment. A key novelty of this approach is that it does not have to select an entire timescale map but rather use the best timescale locally. The approach has real-time performance and is evaluated using three datasets with increasing levels of dynamics. We compare our approach against previously proposed NDT-MCL and commonly used SLAM algorithms and show that DT-NDT-MCL outperforms competing algorithms with regards to accuracy in all three test cases.

2.1.4. HiHeading: Smartphone-based Indoor Map Construction System with High Accuracy Heading Inference
Smartphone is widely used in indoor map construction with its build-in sensors. However, the low accuracy problem of build-in sensors always causes the collected user trajectories noisy. The significant problem we face is the low accuracy of walk heading estimated by build-in sensors because of phone heading fluctuation and magnetic field anomaly. This paper presents HiHeading – a high reliable crowd sourcing-based indoor map construction system. HiHeading leverages build-in inertial sensors to construct accurate motion traces. These traces are generated by HiHeading with high accuracy based on the novel ideal of fusing gyroscope and orientation sensor to get reliable walk heading estimation in indoor dead reckoning (DR). To evaluate our system, we have tested it in a middle size indoor office by recording 3 people’s walk trajectories during 5 days. We present an evaluation of our system and the experiment result shows 70% of the heading estimation error is lower than 10 degrees.

2.1.5. Graph-Based Map Matching for Indoor Positioning
This article presents a probabilistic motion model that is based on an economical graph-based indoor map representation, such that the motion of the user is constrained according to the floor plan of a building. The floor plan is modeled as a combination of links and open space polygons that are connected by nodes. In the authors’ earlier work the link transition probabilities in this graph are proportional to the total link lengths that are the total lengths of the sub graphs accessible by choosing the considered link option, and this article extends this model to include open space polygons as well. A particle filter using the extended motion model in which all particles are constrained according to the map structure is presented. Furthermore, wireless local area network and Bluetooth Low Energy positioning tests show that the proposed algorithm outperforms comparison methods especially if the measurement rate is low.

Methodology
Provide an android app to be used in the secret mission of the police department. The main purpose of this app is to avoid accident during secret mission and make a link between the members in the team. It show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy. This will make the policeman concentrate in their work and help to show the location of other team members in the mission.

Android Studio:
Android Studio is software which is used to develop mobile application. In this application, there are four modules in this application. First module is Authentication; this will provide security for the mobile application by using the data in the database. This is done by matching the given user ID and password with the data in the database. If data were similar then the screen will move on to the next page. Second module is Location Tracking; this will show the location of all the users who were logged in. The location is updated in the database for every movement of the user. Then the location is displayed in the map page. Third module is Safety Lock; this module is to provide security after the user logged in. This is to avoid the usage of application by external access. The map page is locked every ten second for avoiding misuse of the application. Last module is database; this will store the information of the users and also used for authentication.

MODULES
Authentication
Location Tracking
Safety Lock
Database
3.2.1. Authentication
Checking the given user ID and password is matched with the data in the database. If the data is matched then the application is logged into map page.

3.2.2. Location Tracking
Map page display the location of every user who were logged in the application. For every movement the latitude and longitude is updated in the database. By using the data location is changed in the map page.

3.2.3. Mobile Authentication

3.3 SYSTEM ARCHITECTURE
The users enter the user name and password. These information is checked with the data in the database. We have already put the information of the user like (name, ID, password, image). If the data is similar then the map page is login. Else the application was exited. It is used to provide security for the application.

Fig 3.3 System architecture
3.4 ADVANTAGES
This will reduce the cost.

Easy to monitor policemen.

Avoid accident due to misunderstanding.

It is portable.

Accuracy is very high.
Security is high.

4. SYSTEM REQUIREMENTS
4.1. Hardware requirements
PROCESSOR: AMD PRO A4-3350B APU with Radeon R4 Graphics 2.00 GHz.

RAM : 4 GB RAM
HARD DISK : 512 GB
KEYBOARD : 102KEYS
4.2. Software requirements
Operating system : Windows 10
IDE : Android Studio 2.3.3
Front End : XML
Coding Language : Java, PHP
Database : SQL Server
4. Conclusion
By using this application we can easily detect the location of the policeman. It will help us to provide an efficient tracking to monitor other team met in the secret mission. When they were going for the secret mission this application is easy to carry. Because, it is an Android app run in mobile phone.

REFERENCES
1 Kanglian Zhao, Jian Wang, Wenfeng Li, Guangwei Bai, Naitong Zhang “Device-Free Human Localization Using Panoramic Camera and Indoor Map” IEEE International Conference on Consumer Electronics-China (ICCE-China), 2016.

2 Mike Koivisto, Henri Nurminen, Simo Ali-L¨oytty, and Robert Pich´e “Graph-Based Map Matching for Indoor Positioning” IEEE Workshop on Positioning, Navigation and Communication, 2015.

3 Wenneng Ma, Jing Wu, Chengnian Long and Yanmin Zhu “HiHeading: Smartphone-based Indoor Map Construction System with High Accuracy Heading Inference” 11th International Conference on Mobile Ad-hoc and Sensor Networks, 2015.

4 Rafael Valencia, Jari Saarinen, Henrik Andreasson, Joan Vallv´e, Juan Andrade-Cetto and Achim J. Lilienthal “Localization in highly dynamic environments using dual-timescale NDT-MCL” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014.

5 Duc A. Tran, Cuong Pham “Fast and Accurate Indoor Localization based on Spatially Hierarchical Classification” IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems, 2014.

6 K. Zhai, B. Jiang, and W. K. Chan, “Prioritizing test cases for regression testing of location-based services: metrics, techniques, and case study,” IEEE Transactions on Services Computing, vol. 7, no. 6, pp. 54-67, Jan. 2014.

7 Y. Y. Gu, A. Lo, and I. Niemegeers, “A survey of indoor positioning systems for wireless personal networks,” IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp. 13-32, First Quarter 2009.

8 S. N. He and S. H. Chan, “Wi-Fi fingerprint-based indoor positioning: recent advances and comparisons,” IEEE Communications Surveys and Tutorials, vol. 18, no. 1, pp. 466-490, First Quarter 2016.

9 T. V. Nguyen, Y. M. Jeong, D. P. Trinh, and H. Shin, “Location-aware visual radios,” IEEE Wireless Communications, vol. 21, no. 4, pp. 28-36, Aug. 2014.

10 Y. L. Sun and Y. B. Xu, “Error estimation method for matrix correlation based Wi-Fi indoor localization,” KSII Transactions on Internet and Information Systems, vol. 7, no. 11, pp. 2657-2675, Nov. 2013.

11 R. Pflugfelder and H. Bischof, “Localization and trajectory reconstruction
in surveillance cameras with non over lapping views,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp. 709-721,
April 2010.

12 L. Liu, X. Zhang, and H. D. Ma, “Localization-oriented coverage in wireless camera sensor networks,” IEEE Transactions on Wireless Communications,
vol. 10, no. 2, pp. 484-494, Feb. 2011.

13 Y. Liu, Q. Wang, J. B. Liu, J. Chen, and T. Wark, “An efficient and effective localization method for networked disjoint top-view cameras,” IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 9, pp. 2526-2537, Sep. 2013.

14 Y. S. Lin, K. H. Lo, H. T. Chen, and J. H. Chuang, “Vanishing point-based image transforms for enhancement of probabilistic occupancy map-based people localization,” IEEE Transactions on Image Processing, vol. 23, no. 12, pp. 5586-5598, Dec. 2014.

15 S. Y. Chen, J. h. Zhang, Y. F. Li, and J. W. Zhang,”A Hierarchical Model Incorporating Segmented Regions and Pixel Descriptors for Video Background Subtraction,” IEEE Transactions on Industrial Informatics, vol. 8, no. 1, pp. 118-127, Feb. 2012.

16 M. Nixon, Feature extraction and image processing for computer vision, 3rd ed., New York NY: Academic Press, 2012.

17 F. M. Aderibigbe, K. J. Adebayo, and A. O. Dele-Rotimi, “On quasi newton method for solving unconstrained optimization problems,” American Journal of Applied Mathematics, vol. 3, no. 2, pp. 47-50, 2015.

18 K. Bauer, D. McCoy, E. Anderson, M. Breitenbach, G. Grudic, D. Grunwald, and D. Sicker, “The directional attack on wireless localization: how to spoof your location with a tin can,” in Proceedings of the 28th IEEE conference on Global telecommunications, ser. GLOBECOM’09. Piscataway, NJ, USA: IEEE Press, 2009, pp. 4125–4130. Online. Available:http://dl.acm.org/citation.cfm?id=1811982.1812067