Moving Object Classification in a Video Sequence

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Author(s): S. Vasavi (V. R. Siddhartha Engineering College, India), T. Naga Jyothi (V. R. Siddhartha Engineering College, India)and V. Srinivasa Rao (V. R. Siddhartha Engineering College, India)
Copyright: 2017
Pages: 32
Source title: Applied Video Processing in Surveillance and Monitoring Systems
Source Author(s)/Editor(s): Nilanjan Dey (Techno India College of Technology, Kolkata, India), Amira Ashour (Tanta University, Egypt)and Suvojit Acharjee (National Institute of Technology Agartala, India)
DOI: 10.4018/978-1-5225-1022-2.ch004

Keywords: Computer Science & IT / Computer Vision & Image Processing / Information Science Reference / Surveillance Systems

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Abstract

Now-a-day's monitoring objects in a video is a major issue in areas such as airports, banks, military installations. Object identification and recognition are the two important tasks in such areas. These require scanning the entire video which is a time consuming process and hence requires a Robust method to detect and classify the objects. Outdoor environments are more challenging because of occlusion and large distance between camera and moving objects. Existing classification methods have proven to have set of limitations under different conditions. In the proposed system, video is divided into frames and Color features using RGB, HSV histograms, Structure features using HoG, DHoG, Harris, Prewitt, LoG operators and Texture features using LBP, Fourier and Wavelet transforms are extracted. Additionally BoV is used for improving the classification performance. Test results proved that SVM classifier works better compared to Bagging, Boosting, J48 classifiers and works well in outdoor environments.

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