Moving Object Classification in a Video Sequence

Moving Object Classification in a Video Sequence

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
DOI: 10.4018/978-1-5225-1022-2.ch004

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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Introduction

Now-a-day’s monitoring the objects (human beings, animals, buildings, vehicles etc.,) in a video is a major issue in the areas such as airports, banks, military installations etc., Object identification and recognition are considered as two important tasks in such areas. To do these two tasks, video is to be taken with a wide landscape of the scene, which results in a small low resolution and occluded images for objects. Basic video analysis operations such as object detection, classification and tracking require scanning the entire video. But this is a time consuming process and hence we require a method to detect and classify the objects that are present in the frames extracted from a real time video. Outdoor environments are more challenging for moving object classification because of incomplete appearance details of moving objects due to occlusions and large distance between the camera and moving objects. So, there is a need to monitor and classify the moving objects by considering the challenges of video in the real time.

Motivation

Visual surveillance is an active area of research topic because of its importance in the areas such as security, law enforcement, and military applications. Surveillance cameras are installed in sensitive areas such as airports, banks, military installations, railway stations, highways, and in public places. Data that is collected from these cameras have to be monitored either manually or using intelligent systems. Human operators monitoring manually for long durations is infeasible due to monotony and fatigue. As such, recorded videos are inspected when any suspicious event is notified. But this method only helps for recovery and does not avoid any unwanted events. “Intelligent” video surveillance systems can be used to identify various events and to notify concerned personal when any unwanted event is identified. As a result, such a system requires algorithms that are fast, robust and reliable during various phases such as detection, tracking, classification etc. This can be done by implementing a fast and efficient technique to classify the objects that are present in the video in the real time.

Problem Statement

Basic video analysis operations such as object detection, classification and tracking require scanning the entire video. But this is a time consuming process and hence we require a method to detect and classify the objects that are present in the frames extracted from a real time video. Object classification is to be done which is an important building block that significantly impacts reliability of its applications. Outdoor environments are more challenging for moving object classification because of incomplete appearance details of moving objects due to occlusions and large distance between the camera and moving objects. So, there is a need to monitor and classify the moving objects by considering all the challenges as mentioned above and object classification is crucial that is done based on the features extracted from the objects in the video, here classification is a tough task when we consider video in the real time.

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