A robust single and multiple moving object detection, tracking and classification. Issue 1 (29th July 2020)

• Record Type: Journal Article
• Authors: Mahalingam, T.; Subramoniam, M.
• Is Part Of: Applied computing and informatics. Volume 17:Issue 1(2021)
• Page Start: 2
• Page End: 18
• Publication Date: 2020-07-29
• Subjects: Surveillance -- Moving object detection and tracking -- Mixture of Adaptive Gaussian (MoAG) -- Fuzzy morphological filter and blob analysis; Information science -- Periodicals; Information storage and retrieval systems -- Periodicals; 004
• DOI: 10.1016/j.aci.2018.01.001
• Languages: English
• ISSNs: 2210-8327

Abstract

Abstract : Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.