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Robust Detection and Tracking of Objects Using BTC and Cam-Shift Algorithm

  • S. Kayalvizhi
  • B. Mounica
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 490)

Abstract

Face detection is used in several applications in the field of object recognition and pattern recognition tools. It is a demand nowadays that face detection to be performed using the compressed data. In this paper, we discussed on the face detection method applied on compressed images and video streams where only little decompression is required to retrieve the important data. This approach is faster and consumes less computational time and processing power when compared to the pixel domain-based algorithms. We used the Block Truncation Coding (BTC) algorithm for compression process. Viola and Jones proposed a fast and accurate method to detect the object. Haar-like features are used to detect the variation between the black and light portion of the image. Cam-shift algorithm is used to develop the face and head tracking. The object search is done using the back-projection procedure through probability distribution maximum obtained.

Keywords

Viola–Jones algorithm Cam-shift Haar feature selection BTC algorithm 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringSRM UniversityChennaiIndia

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