Video search and indexing with reinforcement agent for interactive multimedia services

Paul, Anand; Chen, Bo-Wei; Bharanitharan, K.; Wang, Jhing-Fa

doi:10.1145/2423636.2423643

Cited by 25 publications

(8 citation statements)

References 23 publications

(15 reference statements)

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“…Finally on using simulation results which show that the end-to-end delay is minimized in cooperative communication according to the vehicle priority. There are lots of challenges ahead when V2V environment meets up M2M with various soft computing systems [32,[43][44][45], in this ear billions f ubiquitous device are connected together to form a huge mesh of big data [46,47] apart from that now a days voice controlled vehicles are facing real-time implementation challenges. …”

Section: Resultsmentioning

confidence: 99%

Cooperative Intelligence of Vehicles for Intelligent Transportation Systems (ITS)

Daniel

Paul

Ahmad

et al. 2015

Wireless Pers Commun

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The aim of Intelligent Transportation Systems (ITS) is to automate the interactions among vehicles and infrastructure to accomplish high levels of safety measures, comfort, and competence in vehicular communication. To utilize the future trends of increasing traffic safety and efficiency in ITS, integrating vehicles and infrastructures with the cooperative vehicular technique will be the feasible solution. In order to demonstrate the importance of cooperative communication in vehicular networks, a spectral efficient architecture has been proposed for cooperative centralized and distributed spectrum sensing in vehicular networks. We discuss the possibilities of Cognitive Radio in the cooperative vehicular environment. In order to exhibit cooperative vehicular networks, hardware modules are designed for a vehicle to vehicle, vehicle to infrastructure and infrastructure to infrastructure communications. Furthermore, quantitative analysis is made in order to calculate the energy optimization, connectivity failure probability and traffic management in cooperative vehicular networks. In addition, we test the results of the cooperative vehicular network by simulating it in NS2. In this respect, we have considered three different cases, Emergency vehicles, VIP vehicles, and normal vehicles. It is inferred from the results that end-to-end delay for emergency vehicles in the cooperative environment is considerably less as compared to VIP and normal vehicles.

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Section: Resultsmentioning

confidence: 99%

Cooperative Intelligence of Vehicles for Intelligent Transportation Systems (ITS)

Daniel

Paul

Ahmad

et al. 2015

Wireless Pers Commun

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“…Occlusion handling for vehicle collision handling was proposed in Paul et al 26 In this article, we propose to use occlusion handling technique to calibrate the camera that takes images before they are processed. The remainder of this section briefly discusses the technologies required in this area of research.…”

Section: Related Workmentioning

confidence: 99%

Machine learning–based automated image processing for quality management in industrial Internet of Things

Rahmatov

Paul

Saeed

et al. 2019

International Journal of Distributed Sensor Networks

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The aim of this article is to automate quality control once a product, essentially a central processing unit system, is manufactured. Creating a model that helps in quality control, increases efficiency and speed of production by rejecting abnormal products automatically is vital. A widely used technology for this is to use industrial image processing that is based on the use of special cameras or imaging systems installed within the production line. In this article, we propose a highly efficient model to automate central processing unit system production lines in an industry such that images of the production lines are scanned and any abnormalities in their assembly are pointed out by the model and information about this is transferred to the system administrator via a cyber-physical cloud system network. A machine learning-based approach is used for proper classification. This model not only focuses on just the abnormalities but also helps in configuring the angles from which images of the production are taken, and our methods show 92% accuracy.

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“…A Paul et al [71] have presented a system for video search and indexing based on SVM. State transition correction technique and transition quality estimation is used in the system.…”

Section: Related Workmentioning

confidence: 99%

Analysis and Detection of Content based Video Retrieval

Gornale¹,

Babaleshwar²,

Babaleshwar³

2019

IJIGSP

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Content Based Video Retrieval (CBVR) System has been investigated over past decade it's rooted in many applications like developments and technologies. The demand for extraction of high level semantics contents as well as handling of low level contents in video retrieval systems are still in need. Hence it motivates and encourages many researchers to discover their knowledge across CBVR domain and contribute their work to make the system more effective and useful in developing the system application. This paper highlights comprehensive and extensive review of CBVR techniques for detection of region of interest in a given video. The experiment is carried out for the detection of ROI using ACF detector. The detection rate of ROI is observed competitive and satisfactory.

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Video search and indexing with reinforcement agent for interactive multimedia services

Cited by 25 publications

References 23 publications

Cooperative Intelligence of Vehicles for Intelligent Transportation Systems (ITS)

Cooperative Intelligence of Vehicles for Intelligent Transportation Systems (ITS)

Machine learning–based automated image processing for quality management in industrial Internet of Things

Analysis and Detection of Content based Video Retrieval

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