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Video Surveillance for the Crime Detection Using Features

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Book cover Advanced Machine Learning Technologies and Applications (AMLTA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1141))

Abstract

This paper aims at extending the comparison between two images and locating the query image in the source image by matching the features in the videos by presenting a method for the recognition of a particular person or an object. The frames matching the feature (not feature its query) object in a given video will be the output. We describe a method to find unique feature points in an image or a frame using SIFT, i.e., scale-invariant feature transform method. SIFT is used for extracting distinctive feature points which are invariant to image scaling or rotation, presence of noise, changes in image lighting, etc. After the feature points are recognized in an image, the image is tracked for comparison with the feature points found in the frames. The feature points are compared using homography estimation search to find the required query image in the frame. In case the object is not present in the frame, then it will not present any output.

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Author information

Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology Karnataka, Mangalore, India

    Aastha Chowdhary & Bhawana Rudra

Authors
  1. Aastha Chowdhary
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  2. Bhawana Rudra
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Corresponding author

Correspondence to Aastha Chowdhary .

Editor information

Editors and Affiliations

  1. Faculty of Computer and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Roheet Bhatnagar

  3. Faculty of Science, Helwan University, Helwan, Egypt

    Ashraf Darwish

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Chowdhary, A., Rudra, B. (2021). Video Surveillance for the Crime Detection Using Features. In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2020. Advances in Intelligent Systems and Computing, vol 1141. Springer, Singapore. https://doi.org/10.1007/978-981-15-3383-9_6

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