A robust single and multiple moving object detection, tracking and classification

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.

Objects detection and tracking using fast principle component purist and kalman filter

“…In the method [13], animated objects are represented as groups of spatial and temporal points using the Gabor 3D filter, which works on the spatial and temporal analysis of the sequential video and is then joint by using the Minimum Spanning Tree. The proposed technique described in [14], split into three stages; Foreground segmentation stage by using Mixture of Adaptive Gaussian model, tracking stage by using the blob detection and evaluation stage which includes the classification according to the feature extraction. The proposed work in [15], it merged Background Subtraction with Low Rank techniques for effective object detection.…”

Implementation of Intelligent Monitoring for the Marshalling Yard

“…Для решения задачи контроля движения вагонов в терминах компьютерного зрения [10] необходимо решить две подзадачи . Они -следующие:…”

Support vector regression and extended nearest neighbor for video object retrieval

“…• Even though several algorithms have been developed in the literature, a problem occurring due to the presence of non-rigid structures in the video has been still unaddressed [8]. • Noise reduction during the object detection model significantly reduces the visualization effects of video [9]. • Some of the particle challenges faced during object tracking are, object to object occlusion, object to scene occlusion, abrupt object motion, various lighting conditions, etc.…”

“…Detection of trajectory path through object detection gets affected due to various noise factors, such as illumination, slow-moving objects, shadows and other phenomena. Object detection mainly carries out noise reduction such that the retrieval process can be made more efficient [9]. One of the commonly used tracking mechanisms is the regionbased target tracking.…”