METHODS OF IMAGE RECOGNITION IN A VIDEO STREAM

The article examines the problem of automatic object recognition using a video stream as a digital image. Algorithms for recognizing and tracking objects in the video stream are considered, methods used in video processing are analyzed, and the use of machine learning tools in working with video is described.The main approaches to solving the problem of recognizing moving objects in a video stream are investigated: the detection-based approach and the tracking-based approach. Arguments are made in favor of the tracking-based approach, and, in addition, modern methods of tracking objects in the video stream are considered. In particular, the algorhythms: Online Boosting Tracker - one of the first object tracking algorithms with high tracking accuracy, MIL Tracker (Multiple Instance Learning Tracker), which is a development of the idea of learning with a teacher and the Online Boosting algorithm and the KCF Tracker algorithm (Kernelized Correlation Filters Tracker) - a method that uses the mathematical properties of overlapping areas of positive examples.As a result, the advantages and disadvantages of the considered methods and algorithms for recognizing and tracking objects for various applications are highlighted.

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Design and Implementation of Large-Scale Video Tracking Software System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.4612 ◽

2014 ◽

Vol 644-650 ◽

pp. 4612-4615

Author(s):

Jun Hui Zhao

Keyword(s):

Video Processing ◽

Large Scale ◽

Moving Objects ◽

Design Method ◽

Detection Algorithm ◽

Video Tracking ◽

Software System ◽

Tracking Accuracy ◽

Tracking Process ◽

Radon Detection

The design of large-scale video tracking software system is studied. With the continuous development of computer video processing technology, large-scale video tracking technology has become very important. This paper presents a design method for large-scale video tracking software system based on Radon detection algorithm. During the video tracking process, numerous video images need to be collected, and then preprocessed with filtering algorithm, through Radon detection method to predict and compensate moving objects trajectory obtained in video to make up for the tracking lag caused by mutated direction. Experimental results show that the proposed algorithm for large-scale video tracking software design can improve the tracking accuracy effectively, and achieve satisfactory results.

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GPS and GSM Based Tracking System for Objects Moving over Wide Geographical Areas

Al-Kitab Journal for Pure Sciences ◽

10.32441/kjps.v2i1.144 ◽

2018 ◽

Vol 2 (1) ◽

Author(s):

Fatima Ameen ◽

Ziad Mohammed ◽

Abdulrahman Siddiq

Keyword(s):

Global Positioning System ◽

Mobile Communications ◽

Moving Objects ◽

Tracking System ◽

Geographical Area ◽

Tracking Systems ◽

Acceptable Result ◽

Internet Service ◽

Global Positioning ◽

Tracking Objects

Tracking systems of moving objects provide a useful means to better control, manage and secure them. Tracking systems are used in different scales of applications such as indoors, outdoors and even used to track vehicles, ships and air planes moving over the globe. This paper presents the design and implementation of a system for tracking objects moving over a wide geographical area. The system depends on the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) technologies without requiring the Internet service. The implemented system uses the freely available GPS service to determine the position of the moving objects. The tests of the implemented system in different regions and conditions show that the maximum uncertainty in the obtained positions is a circle with radius of about 16 m, which is an acceptable result for tracking the movement of objects in wide and open environments.

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A Two-Stage Data Association Approach for 3D Multi-Object Tracking

Sensors ◽

10.3390/s21092894 ◽

2021 ◽

Vol 21 (9) ◽

pp. 2894

Author(s):

Minh-Quan Dao ◽

Vincent Frémont

Keyword(s):

Object Detection ◽

Object Tracking ◽

Moving Objects ◽

Data Association ◽

Autonomous Driving ◽

Tracking Accuracy ◽

Two Stage ◽

Bipartite Matching ◽

3D Object ◽

3D Object Detection

Multi-Object Tracking (MOT) is an integral part of any autonomous driving pipelines because it produces trajectories of other moving objects in the scene and predicts their future motion. Thanks to the recent advances in 3D object detection enabled by deep learning, track-by-detection has become the dominant paradigm in 3D MOT. In this paradigm, a MOT system is essentially made of an object detector and a data association algorithm which establishes track-to-detection correspondence. While 3D object detection has been actively researched, association algorithms for 3D MOT has settled at bipartite matching formulated as a Linear Assignment Problem (LAP) and solved by the Hungarian algorithm. In this paper, we adapt a two-stage data association method which was successfully applied to image-based tracking to the 3D setting, thus providing an alternative for data association for 3D MOT. Our method outperforms the baseline using one-stage bipartite matching for data association by achieving 0.587 Average Multi-Object Tracking Accuracy (AMOTA) in NuScenes validation set and 0.365 AMOTA (at level 2) in Waymo test set.

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Object detection and tracking in video sequences: formalization, metrics and results

Informatics ◽

10.37661/1816-0301-2021-18-1-43-60 ◽

2021 ◽

Vol 18 (1) ◽

pp. 43-60

Author(s):

R. P. Bohush ◽

S. V. Ablameyko

Keyword(s):

Moving Objects ◽

Human Movement ◽

Video Sequences ◽

Multiple Objects ◽

Movement Trajectories ◽

Detection And Tracking ◽

Form Features ◽

One And Many ◽

Tracking Objects

One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the detection and tracking of one and many objects in video. The following metrics are considered: the quality of detection of tracked objects, the accuracy of determining the location of the object in a frame, the trajectory of movement, the accuracy of tracking multiple objects. Based on the considered generalization, an algorithm for tracking people has been developed that uses the tracking through detection method and convolutional neural networks to detect people and form features. Neural network features are included in a composite descriptor that also contains geometric and color features to describe each detected person in the frame. The results of experiments based on the considered criteria are presented, and it is experimentally confirmed that the improvement of the detector operation makes it possible to increase the accuracy of tracking objects. Examples of frames of processed video sequences with visualization of human movement trajectories are presented.

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Visual odometry in local underwater navigation problems

Journal of Physics Conference Series ◽

10.1088/1742-6596/2091/1/012053 ◽

2021 ◽

Vol 2091 (1) ◽

pp. 012053

Author(s):

I M Azhmukhamedov ◽

P I Tamkov ◽

N D Svishchev ◽

A V Rybakov

Keyword(s):

Experimental Studies ◽

Visual Odometry ◽

Ecological Monitoring ◽

Video Stream ◽

Video Data ◽

Work Processes ◽

Underwater Robots ◽

Advantages And Disadvantages ◽

Local Navigation ◽

Slam Algorithm

Abstract The work processes of the ORB-SLAM algorithm are presented. The results of experimental studies on temporal comparisons of the operation of the algorithm with different parameters and cameras are presented. The necessity of forming a visual odometry (VO) system as a local navigation of remote-controlled and autonomous underwater robots has been substantiated. The two most suitable odometry methods in the underwater environment are described, such as their advantages and disadvantages. The work processes of the ORB-SLAM algorithm are presented. The results of experimental studies on temporal comparisons of the operation of the algorithm with different parameters and cameras are presented. The procedure for preparing video data is described: processing a video stream, adjusting camera parameters for calibration. The experiments represent the testing of the ORB-SLAM3 algorithm on a sample of video filmed as part of the ecological monitoring of the Caspian shelf in 2020.

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Feature Fusion with Deep Neural Network in Kernelized Correlation Filters Tracker

10.1109/icset53708.2021.9612567 ◽

2021 ◽

Author(s):

Devira Anggi Maharani ◽

Carmadi Machbub ◽

Pranoto Hidaya Rusmin ◽

Lenni Yulianti

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Feature Fusion ◽

Correlation Filters ◽

Kernelized Correlation Filters

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Distributed Video Coding and Content Analysis for Resource Constraint Multimedia Applications

Ubiquitous Multimedia and Mobile Agents ◽

10.4018/978-1-61350-107-8.ch011 ◽

2012 ◽

pp. 251-273

Author(s):

Praveen Kumar ◽

Amit Pande ◽

Ankush Mittal ◽

Abhisek Mudgal

Keyword(s):

Video Coding ◽

Real Time ◽

Video Processing ◽

Mobile Agents ◽

Video Transmission ◽

Moving Objects ◽

Individual Case ◽

Multimedia Applications ◽

Real Time Processing ◽

Laboratory Setup

Video coding and analysis for low power and low bandwidth multimedia applications has always been a great challenge. The limited computational resources on ubiquitous multimedia devices like cameras along with low and varying bandwidth over wireless network lead to serious bottlenecks in delivering real-time streaming of videos for such applications. This work presents a Content-based Network-adaptive Video-transmission (CbNaVt) framework which can waive off the requirements of low bandwidth. This is done by transmitting important content only to the end user. The framework is illustrated with the example of video streaming in the context of remote laboratory setup. A framework for distributed processing using mobile agents is discussed with the example of Distributed Video Surveillance (DVS). In this regard, the increased computational costs due to video processing tasks like object segmentation and tracking are shared by the cameras and a local base station called as Processing Proxy Server (PPS).However, in a distributed scenario like traffic surveillance, where moving objects is tracked using multiple cameras, the processing tasks needs to be dynamically distributed. This is done intelligently using mobile agents by migrating from one PPS to another for tracking an individual case object and transmitting required information to the end users. Although the authors propose a specific implementation for CbNaVt and DVS systems, the general ideas in design of such systems exemplify the way information can be intelligently transmitted in any ubiquitous multimedia applications along with the use of mobile agents for real-time processing and retrieval of video signal.

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