Methods for real-time object searching and recognizing in video images on ios mobile platform

Kushnir, Dmytro; Paramud, Yaroslav

doi:10.23939/csn2019.01.024

Cited by 5 publications

(3 citation statements)

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“…Similar investigation was done as well for mobile platform [4]. The method of functional adaptation of the algorithm of search and recognition of objects to features of video is offered, which consists in processing of video image by smoothing and minimization filter, was designed and presented.…”

Section: Related Workmentioning

confidence: 89%

Methods of Vehicle Recognition and Detecting Traffic Rules Violations on Motion Picture Based on OpenCV Framework

Fastiuk¹,

Bachynskyy²,

Huzynets³

2021

ACPS

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In this era, people using vehicles is getting increased day by day. As pedestrians leading a dog for a walk, or hurrying to their workplace in the morning, we’ve all experienced unsafe, fast-moving vehicles operated by inattentive drivers that nearly mow us down. Many of us live in apartment complexes or housing neighborhoods where ignorant drivers disregard safety and zoom by, going way too fast. To plan, monitor and also control these vehicles is becoming a big challenge. In the article, we have come up with a solution to the above problem using the video surveillance considering the video data from the traffic cameras. Using computer vision and deep learning technology we will be able to recognize violations of rules. This article will describe modern CV and DL methods to recognize vehicle on the road and traffic violations of rules by them. Implementation of methods can be done using OpenCV Python as a tool. Our proposed solution can recognize vehicles, track their speed and help in counting the objects precisely.

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Section: Related Workmentioning

confidence: 89%

Methods of Vehicle Recognition and Detecting Traffic Rules Violations on Motion Picture Based on OpenCV Framework

Fastiuk¹,

Bachynskyy²,

Huzynets³

2021

ACPS

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“…The result of YOLOv3 recognition is the bounding box depicted on the screen with a probability of recognition. Such recognition results of quality could be increased by using minimization and smoothing filters [10]. The example of such system is depicted in Fig.…”

Section: Fig 2 Animal Playful Pose Analysis Drone Systemmentioning

confidence: 99%

The Algorithm of Cyber-physical System Targeting on a Movable Object Using the Smart Sensor Unit

Kushnir¹,

Paramud²

2017

ACPS

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It is known that smart sensor units are one of the main components of the cyber-physical system. One of the tasks, which have been entrusted to such units, are targeting and tracking of movable objects. The algorithm of targeting on such objects using observation equipment has been considered. This algorithm is able to continuously monitor observation results, predict the direction with the highest probability of movement and form a set of commands to maximize the approximation of a moving object to the center of an information frame. The algorithm has been verified on an experimental physical model using a drone. The object recognition module has been developed using YOLOv3 architecture. iOS application has been developed in order to communicate with the drone through WIFI hotspot using UDP commands. Advanced filters have been added to increase the quality of recognition results. The results of experimental research on the mobile platform confirmed the functioning of the targeting algorithm in real-time.

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“…Recently, algorithms based on the use of convolutional neural networks or Regional Convolutional Neural Networks (R-CNN) have become widespread to solve this problem, which are fundamentally focused on solving the problem of finding objects with their simultaneous classification [7]- [11]. In comparison with the methods presented above, these algorithms are fundamentally adapted to solve the problem of finding objects in images.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Algorithms for Searching Objects in Images Using Convolutional Neural Network

Koval¹

2021

ACPS

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The problem of finding objects in images using modern computer vision algorithms has been considered. The description of the main types of algorithms and methods for finding objects based on the use of convolutional neural networks has been given. A comparative analysis and modeling of neural network algorithms to solve the problem of finding objects in images has been conducted. The results of testing neural network models with different architectures on data sets VOC2012 and COCO have been presented. The results of the study of the accuracy of recognition depending on different hyperparameters of learning have been analyzed. The change in the value of the time of determining the location of the object depending on the different architectures of the neural network has been investigated.

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Methods for real-time object searching and recognizing in video images on ios mobile platform

Cited by 5 publications

References 0 publications

Methods of Vehicle Recognition and Detecting Traffic Rules Violations on Motion Picture Based on OpenCV Framework

Methods of Vehicle Recognition and Detecting Traffic Rules Violations on Motion Picture Based on OpenCV Framework

The Algorithm of Cyber-physical System Targeting on a Movable Object Using the Smart Sensor Unit

Analysis of Algorithms for Searching Objects in Images Using Convolutional Neural Network

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