scholarly journals Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV Remote Sensing

2021 ◽  
Vol 13 (19) ◽  
pp. 3853
Author(s):  
Yiguang Wu ◽  
Meizhen Wang ◽  
Xuejun Liu ◽  
Ziran Wang ◽  
Tianwu Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Video Data ◽  
Experimental Results ◽  
Video Monitoring ◽  
Cycle Performance ◽  
Surveillance Systems ◽  
Construction Time ◽  
Construction Productivity ◽  
Surveillance Camera ◽  
Work Cycle

Monitoring the work cycles of earthmoving excavators is an important aspect of construction productivity assessment. Currently, the most advanced method for the recognition of work cycles is the “Stretching-Bending” Sequential Pattern (SBSP), which is based on fixed-carrier video monitoring (FC-SBSP). However, the application of this method presupposes the availability of preconstructed installation carriers to act as a surveillance camera as well as installed and commissioned surveillance systems that work in tandem with them. Obviously, this method is difficult to apply to projects with no conditions for a monitoring camera installation or which have a short construction time. This highlights the potential application of Unmanned Aerial Vehicle (UAV) remote sensing, which is flexible and mobile. Unfortunately, few studies have been conducted on the application of UAV remote sensing for the work cycle monitoring of earthmoving excavators. This research is necessary because the use of UAV remote sensing for monitoring the work cycles of earthmoving excavators can improve construction productivity and save time and costs, especially in post-disaster reconstruction projects involving harsh construction environments, and emergency projects with short construction periods. In addition, the challenges posed by UAV shaking may have to be taken into account when using the SBSP for UAV remote sensing. To this end, this study used application experiments in which stabilization processing of UAV video data was performed for UAV shaking. The application experimental results show that the work cycle performance of UAV remote-sensing-based SBSP (UAV-SBSP) for UAV video data was 2.45% and 5.36% lower in terms of precision and recall, respectively, without stabilization processing than after stabilization processing. Comparative experiments were also designed to investigate the applicability of the SBSP oriented toward UAV remote sensing. Comparative experimental results show that the same level of performance was obtained for the recognition of work cycles with the UAV-SBSP as compared with the FC-SBSP, demonstrating the good applicability of this method. Therefore, the results of this study show that UAV remote sensing enables effective monitoring of earthmoving excavator work cycles in construction sites where monitoring cameras are not available for installation, and it can be used as an alternative technology to fixed-carrier video monitoring for onsite proximity monitoring.

Detecting “DeepFakes” in H.264 Video Data Using Compression Ghost Artifacts

2020 ◽  
Vol 2020 (4) ◽  
pp. 116-1-116-7
Author(s):  
Raphael Antonius Frick ◽  
Sascha Zmudzinski ◽  
Martin Steinebach
Keyword(s):  
Image Forensics ◽  
Video Data ◽  
Experimental Results ◽  
Video Sequences ◽  
The Internet ◽  
Video Content ◽  
High Quality ◽  
The Public

In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.

Lossless Compression for Text and Images

1997 ◽  
Vol 08 (01) ◽  
pp. 179-231 ◽  
Author(s):  
Alistair Moffat ◽  
Timothy C. Bell ◽  
Ian H. Witten
Keyword(s):  
Remote Sensing ◽  
Lossless Compression ◽  
Lossy Compression ◽  
International Standards ◽  
Experimental Results ◽  
Extensive Discussion ◽  
Standard Methods ◽  
Sensing Applications ◽  
Other Information ◽  
Remote Sensing Applications

Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as images—particularly bilevel ones, or ones arising in medical and remote-sensing applications, or ones that may be required to be certified true for legal reasons. Moreover, during the process of lossy compression, many occasions for lossless compression of coefficients or other information arise. This paper surveys techniques for lossless compression. The process of compression can be broken down into modeling and coding. We provide an extensive discussion of coding techniques, and then introduce methods of modeling that are appropriate for text and images. Standard methods used in popular utilities (in the case of text) and international standards (in the case of images) are described.

Contrast Enhancement and Optimization for Image with Fog

2013 ◽  
Vol 380-384 ◽  
pp. 3778-3781
Author(s):  
Wei Na Huang ◽  
Zheng Xiang Xie
Keyword(s):  
Contrast Enhancement ◽  
Real Time ◽  
Visual Quality ◽  
Experimental Results ◽  
Video Monitoring ◽  
Adaptive Model ◽  
Absorption Effect ◽  
Atmospheric Visibility ◽  
Normal Work ◽  
Computing Speed

Aiming at the absorption effect of fog suspended in the atmosphere on light, the paper established the removing-fog compensation adaptive model which can improve the atmospheric visibility and restore the normal work of outdoor system. The experimental results show that the removing fog image processed by the method of removing-fog compensation optimization can accord with the requirement of human visual, and it can be used in real-time video monitoring as the fast computing speed. The method not only can be used in foggy video which the fog distributed uniformly, and can assess the visual quality for the images processed.

Research of Bus Video Monitoring System Based on WCDMA

2014 ◽  
Vol 543-547 ◽  
pp. 891-894
Author(s):  
Lian Jun Zhang ◽  
Shi Jie Liu
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Transmission System ◽  
Video Data ◽  
Video Monitoring ◽  
Video Server ◽  
Real Time Transmission ◽  
Monitoring Center ◽  
Server System ◽  
Transmission Ability

The bus video monitoring system is composed by WCDMA transmission system, video server system, system monitoring center and outreach system. By WCDMA wireless transmission module achieving real time video data return, while using VPDN network technology. Using of the DVS video server and by WCDMA transmission system, the monitoring videos information will be transmitted to the monitoring center rapidly and in real time. The monitoring center can remotely monitor, manage, and dispatch the bus. The results demonstrating this system has good real time transmission ability.

A Panoramic Video Monitoring System Using Low-Altitude UAV Based on Fish-Eye Lens

2013 ◽  
Vol 341-342 ◽  
pp. 1128-1132
Author(s):  
Zi Ming Xiong ◽  
Ke Li ◽  
Yang Ding ◽  
Gang Wan ◽  
Jin Min Liao
Keyword(s):  
Monitoring System ◽  
Distortion Correction ◽  
Video Data ◽  
Video Monitoring ◽  
Monitoring Method ◽  
Time Dynamic ◽  
Panoramic Video ◽  
Data Source ◽  
Low Altitude ◽  
Fish Eye

This paper uses the panoramic video data obtained by the UAV as data source, constructs low-altitude Panoramic Video Monitoring System based on eye-fish lens, and provides a new monitoring method which is real-time, dynamic, continuous, extensive and measurable by making a breakthrough in some key technologies, such as, distortion correction of eye-fish, geo-location based on panoramic video and projection transformation.

CARRIED BAGGAGE DETECTION AND CLASSIFICATION USING MULTI-TREND BINARY CODE DESCRIPTOR AND SUPPORT VECTOR MACHINE

2021 ◽  
Author(s):  
Shah bano ◽  
Syed Adnan Shah ◽  
Wakeel Ahmad ◽  
Muhammad Ilyas
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Binary Code ◽  
Spatial Information ◽  
Detection Algorithm ◽  
Support Vector ◽  
Surveillance Systems ◽  
Body Parts ◽  
Public Places ◽  
Surveillance Camera

Automatic video surveillance systems have gained significant importance due to an increase in crime rate over the last two decades. Automatic baggage detection through surveillance camera can help in security and monitoring in public places. A detection algorithm for humans (with or without carrying baggage) is proposed in this paper. Detection in the proposed method can be achieved by employing spatial information of the baggage of various texture patterns with locus to the human body carrying it. To extract the features of body parts (such as head, trunk and limbs), the descriptor is exhibited and trained by the support vector machine classifier. The proposed approach has been widely assessed by using publically available datasets. The experimental results have shown that the proposed approach is viable for baggage detection and classification as compared to the other available approaches.

An Intelligent Traffic Engineering Method over Software Defined Networks for Video Surveillance Systems Based on Artificial Bee Colony

2016 ◽  
Vol 12 (4) ◽  
pp. 45-62 ◽  
Author(s):  
Reza Mohammadi ◽  
Reza Javidan
Keyword(s):  
Video Surveillance ◽  
Traffic Engineering ◽  
Surveillance System ◽  
Artificial Bee Colony ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Video Data ◽  
Surveillance Systems ◽  
Bee Colony

In applications such as video surveillance systems, cameras transmit video data streams through network in which quality of received video should be assured. Traditional IP based networks cannot guarantee the required Quality of Service (QoS) for such applications. Nowadays, Software Defined Network (SDN) is a popular technology, which assists network management using computer programs. In this paper, a new SDN-based video surveillance system infrastructure is proposed to apply desire traffic engineering for practical video surveillance applications. To keep the quality of received videos adaptively, usually Constraint Shortest Path (CSP) problem is used which is a NP-complete problem. Hence, heuristic algorithms are suitable candidate for solving such problem. This paper models streaming video data on a surveillance system as a CSP problem, and proposes an artificial bee colony (ABC) algorithm to find optimal solution to manage the network adaptively and guarantee the required QoS. The simulation results show the effectiveness of the proposed method in terms of QoS metrics.

Constructing and Utilizing Video Ontology for Accurate and Fast Retrieval

2011 ◽  
Vol 2 (4) ◽  
pp. 59-75 ◽  
Author(s):  
Kimiaki Shirahama ◽  
Kuniaki Uehara
Keyword(s):  
Knowledge Base ◽  
Large Scale ◽  
Video Retrieval ◽  
Computational Cost ◽  
Semantic Content ◽  
Video Data ◽  
Experimental Results ◽  
Huge Number ◽  
Dempster Shafer Theory ◽  
Shafer Theory

This paper examines video retrieval based on Query-By-Example (QBE) approach, where shots relevant to a query are retrieved from large-scale video data based on their similarity to example shots. This involves two crucial problems: The first is that similarity in features does not necessarily imply similarity in semantic content. The second problem is an expensive computational cost to compute the similarity of a huge number of shots to example shots. The authors have developed a method that can filter a large number of shots irrelevant to a query, based on a video ontology that is knowledge base about concepts displayed in a shot. The method utilizes various concept relationships (e.g., generalization/specialization, sibling, part-of, and co-occurrence) defined in the video ontology. In addition, although the video ontology assumes that shots are accurately annotated with concepts, accurate annotation is difficult due to the diversity of forms and appearances of the concepts. Dempster-Shafer theory is used to account the uncertainty in determining the relevance of a shot based on inaccurate annotation of this shot. Experimental results on TRECVID 2009 video data validate the effectiveness of the method.

scholarly journals Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge

2020 ◽  
Vol 10 (18) ◽  
pp. 6391
Author(s):  
Dien Van Nguyen ◽  
Jaehyuk Choi
Keyword(s):  
Data Extraction ◽  
Video Data ◽  
Transportation Safety ◽  
High Definition ◽  
Video Analytics ◽  
Surveillance Camera ◽  
Novel Approach ◽  
Cloud Server ◽  
Delay Sensitivity ◽  
Automated Tools

Intelligent video analytics systems have come to play an essential role in many fields, including public safety, transportation safety, and many other industrial areas, such as automated tools for data extraction, and analyzing huge datasets, such as multiple live video streams transmitted from a large number of cameras. A key characteristic of such systems is that it is critical to perform real-time analytics so as to provide timely actionable alerts on various tasks, activities, and conditions. Due to the computation-intensive and bandwidth-intensive nature of these operations, however, video analytics servers may not fulfill the requirements when serving a large number of cameras simultaneously. To handle these challenges, we present an edge computing-based system that minimizes the transfer of video data from the surveillance camera feeds on a cloud video analytics server. Based on a novel approach of utilizing the information from the encoded bitstream, the edge can achieve low processing complexity of object tracking in surveillance videos and filter non-motion frames from the list of data that will be forwarded to the cloud server. To demonstrate the effectiveness of our approach, we implemented a video surveillance prototype consisting of edge devices with low computational capacity and a GPU-enabled server. The evaluation results show that our method can efficiently catch the characteristics of the frame and is compatible with the edge-to-cloud platform in terms of accuracy and delay sensitivity. The average processing time of this method is approximately 39 ms/frame with high definition resolution video, which outperforms most of the state-of-the-art methods. In addition to the scenario implementation of the proposed system, the method helps the cloud server reduce 49% of the load of the GPU, 49% that of the CPU, and 55% of the network traffic while maintaining the accuracy of video analytics event detection.

Real-Time Bus Video Monitoring System Based on 3G Network

2013 ◽  
Vol 380-384 ◽  
pp. 790-793
Author(s):  
Min Feng ◽  
Jie Sun ◽  
Yin Yang Zhang
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Video Transmission ◽  
Video Data ◽  
Video Monitoring ◽  
Network Bandwidth ◽  
Real Time Traffic ◽  
3G Network ◽  
Transmission Quality ◽  
Video Transmission Quality

According to some bottleneck problems of the communication network bandwidth in wireless video transmission, a design scheme of real-time traffic video monitoring system based on 3G network is put forward in this paper. The design of hardware and the software realization of the system process are mainly introduced. TMS320DM8168 is selected to build the hardware platform in this system. H.264 video encoder is integrated internally. The real-time transmission of video data is sent to the remote monitoring center through the 3G network to improve the video transmission quality. The system meets the requirements of video transmission applied in automobiles.

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