International Journal of Computer Engineering and Applications, Volume XI, Special Issue, May 17, www.ijcea.com ISSN 2321-3469 SURVEY ON SMART ANALYSIS OF CCTV SURVEILLANCE Nikita Chavan 1,Mehzabin Shaikh 2,Priyanka Mahangare 3, Ketki Keskar 4, Deepali Maske 5 1 Department of Information Technology 2 JSPM s Rajarshi Shahu College Of Engineering Pune, India ABSTRACT: As security and time are the very important aspects for everyone. So there should be some system which helps everyone to be safe. Also if any intrusion occurs we can analyze it in less time. This system helps to detect change detection by analyzing it in less time. Environmental changes can be detected up to some extent. Automation object detection analysis is usually the first task in multi camera surveillance. If there are changes then with help of CCTV recording using Open-CV it is detected and notified with some mark using different color. For analysis of these video on single machine it takes long time, to overcome this problem we are using Hadoop. It is used as it has advantage of distributed processing and centralized server processing. Keywords: Image processing, video analysis, detection, Hadoop, Open-CV. [1] INTRODUCTION Image processing is a technique which takes input as a series of images or a video such as a video frame, processes it using some mathematical operation and helps to obtain the relevant information from the images. Object detection is the fundamental task of CCTV video surveillance and for this we are going to use image processing algorithms. Surveillance involves the monitoring of data. Surveillance cameras are video cameras used for the purpose of observing an area. Camera recordings required human personnel to monitor camera footage. Our system avoids this requirement to monitor camera footage. As due to environmental effect the results may get affect. Using gray scale method we try to reduce the environmental effect up to some extent. We focus on implementation of more efficient system which could cover the drawback of traditional
REVIEW ON INTELLIGENT SURVEILLANCE SYSTEM USING HADOOP method of video surveillance. Our main objective is to develop reliable and robust change detection analysis system and to discourage criminals from doing illegal activities. Using Open CV video recording and image comparing with template image takes place. If there is difference between template image and current image then, the intrusion is detected. This intruded video is stored on server for analysis. Hadoop is used for further analysis. Using Hadoop we minimize the drawback of traditional CCTV surveillance method as the analysis time require and storage requires reduces drastically. As the system shows the part of the video in which the change is detected with the red color. [2] LITERATURE SURVEY A. New Object Detection, Tracking and Recognition Approaches for Video Surveillance Over Camera Network.(2015) In this paper, they present new approaches for object detection and tracking in camera network which improves results than conventional method. For detection they used color based MS segmentation which improves segmentation of objects. The segmented object tracked by BKF-SGM_IMS which avoids exponential complexities with good performance and low arithmetic complexity. The performance of both non-training and training based object recognition algorithms can be improved by this new approach. [1] B. Counting Occurrences of Textual Words in Lecture Video Frames using Apache Hadoop Framework.(2015) In this paper, textual words are recognized and occurrence of each textual word is counted with the help of mapper and reducer function in which mapper function recognized words using TESSERACT recognizer and reducer uses this information to return no. of words present in video. TESSERACT is an optical character recognition engine for various operating systems. It is free software, released under the Apache License. TESSERACT does not come with a GUI and is instead run from the commandline interface. [2] C. A Study on Surveillance Video Abstraction Techniques.(2015) In this paper, video abstraction provides efficient and fast browsing of video contents by using four techniques such as feature based, event based, cluster based and trajectory based. [3] D. Image Processing Techniques for Object Tracking in Video Surveillance- A Survey.(2015)
International Journal of Computer Engineering and Applications, Volume XI, Special Issue, May 17, www.ijcea.com ISSN 2321-3469 A video tracking is the process of locating a moving object or multiple objects over a time using camera. In this paper, how objects can be represented with important feature descriptor is explained and also tracking process is explained from detection, recognition till object tracking types such as region based, active contour based and feature based with their positive and negative aspects. [4] [3] PROPOSED SYSTEM ARCHITECTURE In the above Proposed System the video is captured continuously using CCTV camera. CCTV (Closed Circuit TV) uses one or more video cameras to transmit video and sometimes audio images to a monitor, set of monitors, or video recorder. The difference between CCTV and standard TV is that standard TV openly broadcasts signals to the public. CCTV is not openly transmitted to the public. The analysis of the video will be made with the help of the OpenCV. Then the video will be uploaded on the Hadoop. Here the video would be divided into Number of chunks for further analysis purpose. Using map reduce technology the changes will be detected. Output containing detected date time and number of objects changed in the video will be stored in the text file format and uploaded on the server. Using parsing technique the output will be applied on the video and the changes will be detected which will be useful for quick analysis purpose. The time required will be saved due to Hadoop. The processes are done with the help of algorithms. The algorithms used will be SSIM Index, Mean Square and Color Histogram. [4]PROPOSED WORK Usually CCTV Camera captures video for 24 hours continuously and if we go to analyze whole video it will require large amount of time. So to overcome this problem this
REVIEW ON INTELLIGENT SURVEILLANCE SYSTEM USING HADOOP System is proposed where we will analyze video using Hadoop. For this the following processes should be followed: 1) Record Video: Video Recording takes place using CCTV Cameras. We compare captured image with template image to detect the intrusion, once it is found then it stores the video on the Server. Analysis is performed using Hadoop technology. 2) Extraction of Frame: Historic CCTV Video are of large size, to process this video on single machine it takes long time. To overcome this draw back we use Hadoop technology. 3) Video Analysis: In this video Analysis change detection is done using frame comparison. For this Color Histogram is used which detects the changes in videos. 4) Processing using Hadoop: We use concept of map reduce. It splits the input video into the separate chunks and process them parallel. 5) Parsing :Processed output is stored on server in the form of text file format. Text file is downloaded and the changes are indicated using color. In the figure (2) we can show the scene change detection by completing the proposed work successfully. With the help of image comparison we will be able to detect the changes occurred. As mentioned in the figure (2) frames will be compared using the algorithms. As Hadoop will be used Scene change detection analysis will be done in the less time as compared to traditional methods. The result will be indicated red color mark. Figure:2. Scene Change detection [5]OBJECTIVE Our objective is to make the system more reliable and robust for object detection and to discourage criminals from doing illegal activities. The proposed system is aimed to detect object using multi camera video surveillance. Also make use of map reduce to analyze chunks formed from the stored video. With the help of Hadoop it minimizes the analysis time and cost required.
International Journal of Computer Engineering and Applications, Volume XI, Special Issue, May 17, www.ijcea.com ISSN 2321-3469 [6]CONCLUSION After implementation there may be many advantages and disadvantages. But this System will give better improvement result as compared to the previously implemented system. In the new implementation system as we use Hadoop there will be reduction in time and cost. And the Environmental changes will be detected up to some extent. REFERENCES [1] S. Zhang, C. Wang, S.-C. Chan, X. Wei and C.-H. Ho, New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Over Camera Network, IEEE Sensors Journal, May 2015, pp. 2679 2691. [2] M. Husain, Meena S M, A.K. Sabarad, H. Hebballi, S.M. Nagaralli and S. Shetty, Counting occurrences of textual words in lecture video frames using Apache Hadoop Framework, in Proc. IEEE International Advance Computing Conference, June 2015, pp. 1144 1147. [3] F. F. Chamasemani, L. S. Affendey, N. Mustapha and F. Khalid, A study on surveillance video abstraction techniques, in Proc. IEEE International Conference on Control System, Computing and Engineering, Nov. 2015, pp. 470 475. [4] S. Ojha and S. Sakhare, Image processing techniques for object tracking in video surveillance- A survey, in Proc. International Conference on Pervasive Computing, Jan. 2015,pp. 1 6. [5] Z. Xu and H. Chen, MapReduce based content searching of surveillance system videos, in Proc. IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing, July 2015, pp. 250 254. [6] Y. Satoh, T. Okatani and K. Deguchi, A color-based probabilistic tracking by using graphical models, in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004, pp. 631 636. [7] Y. Zhao, H. Shi, X. Chen, X. Li and C. Wang, An overview of object detection and tracking, in Proc. IEEE International Conference on Information and Automation, Aug. 2015, pp. 280 286. [8] T. D. Gamage, J. G. Samarawickrama, R. Rodrigo and A. A. Pasqual, Image filtering with MapReduce in pseudo-distribution mode, in Proc. Moratuwa Engineering Research Conference, April 2015, pp. 160 164. [9] D. Kangin, D. Kolev and G. Markarian, Multiple video object tracking using variational inference, in Proc. Sensor Data Fusion: Trends, Solutions, Applications, Oct. 2015, pp. 1 6.
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