Research on moving object detection and tracking

Xiao, Limin; Li, Tongqiang

doi:10.1109/fskd.2010.5569825

Cited by 5 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…22 Basically, object tracking is used to find the location of the target in different frames in a sequence of images. The main work of target tracking is to select good target characteristics and use appropriate searching methods.…”

Section: Moving Object Detection and Trackingmentioning

confidence: 99%

Motion tracking system in video based on extensive feature set

Uke

Thool

2013

The Imaging Science Journal

View full text Add to dashboard Cite

In recent years, object detection and tracking has been a dynamic research area. Rapid development of the multimedia and the associated technologies urge the processing of a huge database of video clips. The processing efficiency lies on the search methodologies utilised in the video processing system. Usage of unsuitable search methodologies may make the processing system ineffective. Hence, effective object detection and tracking system is an essential criterion for searching relevant videos from a huge collection of videos. This paper proposes a unique object detection and tracking system where video segmentation, feature extraction, object detection and tracking are combined perfectly using various features. Initially, the database video clips are segmented into different shots before performing the feature extraction process. The proposed system consists of two stages, namely, feature extraction and tracking of object in the video clips. In the feature extraction stage, firstly, colour feature is extracted based on colour quantisation. Next, edge density feature is extracted for the objects present in the query video. Then, the texture feature is extracted based on LGXP technique. Finally, based on these feature extracted, the object will be detected and the detected objects will be tracked by utilising both forward and backward tracking technique. The proposed methodology proved to be more effective and accurate in object detection and tracking.

show abstract

Section: Moving Object Detection and Trackingmentioning

confidence: 99%

Motion tracking system in video based on extensive feature set

Uke

Thool

2013

The Imaging Science Journal

View full text Add to dashboard Cite

show abstract

“…Firstly, difference k D from the current frame k f and the background model frame k b is determined by using equation 1 [18].…”

Section: Fig 2 Categorization Of Vision Based Vehicle Detection Metmentioning

confidence: 99%

“…Whereas, too low value of T introduces a lot of noise. Generally, threshold is chosen through gray histogram by finding double peaks or more and then selecting the bottom value between two peak values [18]. Foreground detection is an essential step in vision based detection.…”

Section: Fig 2 Categorization Of Vision Based Vehicle Detection Metmentioning

confidence: 99%

Computer Vision Based Traffic Monitoring System for Multi-track Freeways

Iftikhar

Dissanayake

Vial

2014

Intelligent Computing Methodologies

View full text Add to dashboard Cite

Nowadays, development is synonymous with construction of infrastructure. Such road infrastructure needs constant attention in terms of traffic monitoring as even a single disaster on a major artery will disrupt the way of life. Humans cannot be expected to monitor these massive infrastructures over 24/7 and computer vision is increasingly being used to develop automated strategies to notify the human observers of any impending slowdowns and traffic bottlenecks. However, due to extreme costs associated with the current state of the art computer vision based networked monitoring systems, innovative computer vision based systems can be developed which are standalone and efficient in analyzing the traffic flow and tracking vehicles for speed detection. In this article, a traffic monitoring system is suggested that counts vehicles and tracks their speeds in realtime for multi-track freeways in Australia. Proposed algorithm uses Gaussian mixture model for detection of foreground and is capable of tracking the vehicle trajectory and extracts the useful traffic information for vehicle counting. This stationary surveillance system uses a fixed position overhead camera to monitor traffic.

show abstract

“…Last, we build the effectiveness detection mechanism for basketball area tracing. If the tracing is effective, use the area to update object template; otherwise, segment the basketball and use the cut basketball area to re-create basketball object template [5].…”

Section: Introductionmentioning

confidence: 99%