scholarly journals OBJECT TRACKING CONTROL USING A GIMBAL MECHANISM

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 189-196
Author(s):  
D. J. Regner ◽  
J. D. Salazar ◽  
P. V. Buschinelli ◽  
M. Machado ◽  
D. Oliveira ◽  
...  
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Tracking Control ◽  
Image Plane ◽  
Target Object ◽  
Control Solution ◽  
Detection Model ◽  
Rotation Matrices ◽  
Camera Orientation ◽  
Object Inspection

Abstract. This work describes a control solution for real time object tracking in images acquired for a RPAS on an object inspection environment. This, controlling a 3-axis gimbal mechanism to control a camera orientation embedded to a RPAS, using its image processed for feedback. The objective of control is to maintain the target of interest at the center of the image plane. The proposed solution uses a YOLOv3 object detection model in order to detect the target object and determine, thru rotation matrices, the new desired angles to converge the object’s position to the center of the image. To compare results of the proposed control, a linear control was tuned using a linear PI algorithm. Simulation and practice experiments successfully tracked the desired object in real time using YOLOv3 in both control approaches presented.

CU-Track: A Multi-Camera Framework for Real-Time Multi-Object Tracking

2013 ◽  
Vol 415 ◽  
pp. 325-332
Author(s):  
Pongsakon Bamrungthai ◽  
Viboon Sangveraphunsiri
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
High Speed ◽  
Nearest Neighbor ◽  
Data Transfer ◽  
Target Object ◽  
Processing Unit ◽  
Process Data ◽  
Pc Cluster ◽  
High Speed Data

This paper presents CU-Track, a multi-camera framework for real-time multi-object tracking. The developed framework includes a processing unit, the target object, and the multi-object tracking algorithm. A PC-cluster has been developed as the processing unit of the framework to process data in real-time. To setup the PC-cluster, two PCs are connected by using PCI interface cards that memory can be shared between the two PCs to ensure high speed data transfer and low latency. A novel mechanism for PC-to-PC communication is proposed. It is realized by a dedicated software processing module called the Cluster Module. Six processing modules have been implemented to realize system operations such as camera calibration, camera synchronization and 3D reconstruction of each target. Multiple spherical objects with the same size are used as the targets to be tracked. Two configurations of them, active and passive, can be used for tracking by the system. The algorithm based on Kalman filter and nearest neighbor searching is developed for multi-object tracking. Two applications have been implemented on the system, which confirm the validity and effectiveness of the developed framework.

Real‐time target object tracking via dual‐directional scaling state predicting

2019 ◽  
Vol 55 (14) ◽  
pp. 791-793
Author(s):  
Tong Liu ◽  
Xiaochun Cao ◽  
Jianmin Jiang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Target Object

scholarly journals Content-Aware Focal Plane Selection and Proposals for Object Tracking on Plenoptic Image Sequences

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 48 ◽  
Author(s):  
Dae Bae ◽  
Jae Kim ◽  
Jae-Pil Heo
Keyword(s):  
Object Tracking ◽  
Autonomous Vehicles ◽  
Focal Plane ◽  
Fundamental Problem ◽  
Image Plane ◽  
Target Object ◽  
Image Sequences ◽  
Selection Algorithm ◽  
Practical Applications ◽  
Prior Art

Object tracking is a fundamental problem in computer vision since it is required in many practical applications including video-based surveillance and autonomous vehicles. One of the most challenging scenarios in the problem is when the target object is partially or even fully occluded by other objects. In such cases, most of existing trackers can fail in their task while the object is invisible. Recently, a few techniques have been proposed to tackle the occlusion problem by performing the tracking on plenoptic image sequences. Although they have shown promising results based on the refocusing capability of plenoptic images, there is still room for improvement. In this paper, we propose a novel focus index selection algorithm to identify an optimal focal plane where the tracking should be performed. To determine an optimal focus index, we use a focus measure to find maximally focused plane and a visual similarity to capture the plane where the target object is visible, and its appearance is distinguishably clear. We further use the selected focus index to generate proposals. Since the optimal focus index allows us to estimate the distance between the camera and the target object, we can more accurately guess the scale changes of the object in the image plane. Our proposal algorithm also takes the trajectory of the target object into account. We extensively evaluate our proposed techniques on three plenoptic image sequences by comparing them against the prior tracking methods specialized to the plenoptic image sequences. In experiments, our method provides higher accuracy and robustness over the prior art, and those results confirm that the merits of our proposed algorithms.

Analysis of visual object tracking algorithms for real-time systems

2021 ◽  
pp. 59-65
Author(s):  
Mykola Moroz ◽  
Denys Berestov ◽  
Oleg Kurchenko
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Video Stream ◽  
Target Object ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Security Systems ◽  
Visual Support ◽  
Tracking Object ◽  
Time Systems

The article analyzes the latest achievements and decisions in the process of visual support of the target object in the field of computer vision, considers approaches to the choice of algorithm for visual support of objects on video sequences, highlights the main visual features that can be based on tracking object. The criteria that influence the choice of the target object-tracking algorithm in real time are defined. However, for real-time tracking with limited computing resources, the choice of the appropriate algorithm is crucial. The choice of visual tracking algorithm is also influenced by the requirements and limitations for the monitored objects and prior knowledge or assumptions about them. As a result of the analysis, the Staple tracking algorithm was preferred, according to the criterion of speed, which is a crucial indicator in the design and development of software and hardware for automated visual support of the object in real-time video stream for various surveillance and security systems, monitoring traffic, activity recognition and other embedded systems.

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

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