New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Using Hadoop
Abstract & Details
Research Area
Data Mining
Keywords
Video analytics
detection
tracking
recognition
Bayesian Kalman Filter
Abstract
Object detection and tracking are two fundamental tasks in multicamera surveillance. The most important technique
of this multicamera related technique is to track and analyze objects within the images. The core technology of
multicamera analysis is used in detecting, analyzing, and tracking the object’s motion. In addition, when the light’s
color or direction changes, it is difficult to trace the object.Firstly use the block based algorithm for detecting the
change scene in video if the scene is change is detected then video is stored on the server for further analysis. Once
the video was stored on the server. Stored videos are dived in to chunks and send to different nodes for analysis
using map reduce technology of Hadoop. for detecting object, we apply algorithms like SSIM index, Histogram
matching Using Hadoop we minimize the analysis time Finally draw the graphs in which show the no of objects to
be detected and time to be required for analysisand stored analysis result into database for security purpose.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Mohan Shinde | SKN Sinhgad Institute of Technology And Science, Lonavala,Pune |
| 2 | ShashankSupe | SKN Sinhgad Institute of Technology And Science, Lonavala,Pune |
| 3 | Sushant Shelar | SKN Sinhgad Institute of Technology And Science, Lonavala,Pune |
| 4 | Mayur Shinde | SKN Sinhgad Institute of Technology And Science, Lonavala,Pune |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Shinde, Mohan, ShashankSupe, Shelar, Sushant, & Shinde, Mayur (2016). New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Using Hadoop. International Journal of Advance Research and Innovative Ideas In Education, 2(3), 2797-2802.
MLA Style
Shinde, Mohan, et al. "New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Using Hadoop." International Journal of Advance Research and Innovative Ideas In Education, vol. 2, no. 3, 2016, pp. 2797-2802.
IEEE Style
Mohan Shinde, ShashankSupe, Sushant Shelar, and Mayur Shinde, "New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Using Hadoop," International Journal of Advance Research and Innovative Ideas In Education, vol. 2, no. 3, pp. 2797-2802, 2016.
Vancouver Style
Shinde Mohan, ShashankSupe, Shelar Sushant, Shinde Mayur. New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Using Hadoop. International Journal of Advance Research and Innovative Ideas In Education. 2016;2(3):2797-2802.
Harvard Style
Shinde, Mohan, ShashankSupe, Shelar, Sushant, & Shinde, Mayur (2016) 'New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Using Hadoop', International Journal of Advance Research and Innovative Ideas In Education, 2(3), pp. 2797-2802.
Chicago Style
Shinde, Mohan, et al. "New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Using Hadoop." International Journal of Advance Research and Innovative Ideas In Education 2, no. 3 (2016): 2797-2802.
Turabian Style
Shinde, Mohan, et al. "New Object Detection, Tracking, and Recognition Approaches for Video Surveillance Using Hadoop." International Journal of Advance Research and Innovative Ideas In Education 2, no. 3 (2016): 2797-2802.
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