scholarly journals Invariant Hough Random Ferns for Object Detection and Tracking

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Yimin Lin ◽  
Naiguang Lu ◽  
Xiaoping Lou ◽  
Fang Zou ◽  
Yanbin Yao ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Large Set ◽  
Detection And Tracking ◽  
Object Appearance ◽  
Benchmark Datasets ◽  
Hough Voting ◽  
Online Tracking

This paper introduces an invariant Hough random ferns (IHRF) incorporating rotation and scale invariance into the local feature description, random ferns classifier training, and Hough voting stages. It is especially suited for object detection under changes in object appearance and scale, partial occlusions, and pose variations. The efficacy of this approach is validated through experiments on a large set of challenging benchmark datasets, and the results demonstrate that the proposed method outperforms state-of-the-art conventional methods such as bounding-box-based and part-based methods. Additionally, we also propose an efficient clustering scheme based on the local patches’ appearance and their geometric relations that can provide pixel-accurate, top-down segmentations from IHRF back-projections. This refined segmentation can be used to improve the quality of online object tracking because it avoids the drifting problem. Thus, an online tracking framework based on IHRF, which is trained and updated in each frame to distinguish and segment the object from the background, is established. Finally, the experimental results on both object segmentation and long-term object tracking show that this method yields accurate and robust tracking performance in a variety of complex scenarios, especially in cases of severe occlusions and nonrigid deformations.

scholarly journals Deteksi Objek Berwarna Merah Secara Real Time Dengan Algoritma Color Filtering

2019 ◽  
Vol 3 (2) ◽  
pp. 140
Author(s):  
Yona Fransiska Dewi ◽  
Nurul Fadillah
Keyword(s):  
Image Processing ◽  
Research And Development ◽  
Object Detection ◽  
Object Tracking ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Processing Techniques ◽  
Detection And Tracking ◽  
Web Camera ◽  
Processing Techniques

The various knowledge and techniques of digital image processing currently available vary greatly. Research and development has been carried out towards object detection and tracking. Color is one of the parameters used to detect and track objects. Humans can distinguish a color, but a computer may not necessarily recognize that color. Digital image processing techniques that can recognize colors, one of which is color filtering. In this study, Color filtering is a technique of processing digital images based on specific colors, detecting and tracking colors by using a web camera (webcam) and red objects. Object Tracking is the process of following an object that moves and moves position, so that the colored object being tracked will draw in realtime with the results of the colors that can be selected.

Object Tracking Algorithm based on Improved Siamese Convolutional Networks Combined with Deep Contour Extraction and Object Detection Under Airborne Platform

2020 ◽  
Vol 64 (4) ◽  
pp. 40409-1-40409-11
Author(s):  
Xiuyan Tian ◽  
Haifang Li ◽  
Hongxia Deng
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Clustering Algorithm ◽  
Object Perception ◽  
Tracking Algorithm ◽  
Tracking Performance ◽  
Contour Extraction ◽  
Data Set ◽  
Siamese Network ◽  
Detection And Tracking

Abstract Object detection and tracking is an indispensable module in airborne optoelectronic equipment, and its detection and tracking performance is directly related to the accuracy of object perception. Recently, the improved Siamese network tracking algorithm has achieved excellent results on various challenging data sets. However, most of the improved algorithms use local fixed search strategies, which cannot update the template. In addition, the template will introduce background interference, which will lead to tracking drift and eventually cause tracking failure. In order to solve these problems, this article proposes an improved fully connected Siamese tracking algorithm combined with object contour extraction and object detection, which uses the contour template of the object instead of the bounding-box template to reduce the background clutter interference. First, the contour detection network automatically obtains the closed contour information of the object and uses the flood-filling clustering algorithm to obtain the contour template. Then, the contour template and the search area are fed into the improved Siamese network to obtain the optimal tracking score value and adaptively update the contour template. If the object is fully obscured or lost, the YoLo v3 network is used to search the object in the entire field of view to achieve stable tracking throughout the process. A large number of qualitative and quantitative simulation results on benchmark test data set and the flying data set show that the improved model can not only improve the object tracking performance under complex backgrounds, but also improve the response time of airborne systems, which has high engineering application value.

scholarly journals Parallel tracking and detection for long-term object tracking

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090257
Author(s):  
Dan Xiong ◽  
Huimin Lu ◽  
Qinghua Yu ◽  
Junhao Xiao ◽  
Wei Han ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Art Object ◽  
High Processing Speed ◽  
Online Learning Algorithms ◽  
Integrate State ◽  
Unknown Object

High tracking frame rates have been achieved based on traditional tracking methods which however would fail due to drifts of the object template or model, especially when the object disappears from the camera’s field of view. To deal with it, tracking-and-detection-combination has become more and more popular for long-term unknown object tracking, whose detector almost does not drift and can regain the disappeared object when it comes back. However, for online machine learning and multiscale object detection, expensive computing resources and time are required. So it is not a good idea to combine tracking and detection sequentially like Tracking-Learning-Detection algorithm. Inspired from parallel tracking and mapping, this article proposes a framework of parallel tracking and detection for unknown object tracking. The object tracking algorithm is split into two separate tasks—tracking and detection which can be processed in two different threads, respectively. One thread is used to deal with the tracking between consecutive frames with a high processing speed. The other thread runs online learning algorithms to construct a discriminative model for object detection. Using our proposed framework, high tracking frame rates and the ability of correcting and recovering the failed tracker can be combined effectively. Furthermore, our framework provides open interfaces to integrate state-of-the-art object tracking and detection algorithms. We carry out an evaluation of several popular tracking and detection algorithms using the proposed framework. The experimental results show that different tracking and detection algorithms can be integrated and compared effectively by our proposed framework, and robust and fast long-term object tracking can be realized.

Re-identification framework for long term visual object tracking based on object detection and classification

2020 ◽  
Vol 88 ◽  
pp. 115969 ◽  
Author(s):  
Paraskevi Nousi ◽  
Danai Triantafyllidou ◽  
Anastasios Tefas ◽  
Ioannis Pitas
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Visual Object ◽  
Visual Object Tracking

OBJECT MOTION DETECTION AND TRACKING BY AN ARTIFICIAL INTELLIGENCE APPROACH

2008 ◽  
Vol 22 (05) ◽  
pp. 915-928 ◽  
Author(s):  
LUCIA MADDALENA ◽  
ALFREDO PETROSINO ◽  
ALESSIO FERONE
Keyword(s):  
Artificial Intelligence ◽  
Object Detection ◽  
Object Tracking ◽  
Moving Object Detection ◽  
Multiple Hypothesis Testing ◽  
Object Motion ◽  
Moving Object ◽  
Surveillance Systems ◽  
Robust Detection ◽  
Detection And Tracking

The aim of this paper is to propose an artificial intelligence based approach to moving object detection and tracking. Specifically, we adopt an approach to moving object detection based on self organization through artificial neural networks. Such approach allows to handle scenes containing moving backgrounds and gradual illumination variations, and achieves robust detection for different types of videos taken with stationary cameras. Moreover, for object tracking we propose a suitable conjunction between Kalman filtering, properly instanced for the problem at hand, and a matching model belonging to the class of Multiple Hypothesis Testing. To assess the validity of our approach, we experimented both proposed moving object detection and object tracking over different color video sequences that represent typical situations critical for video surveillance systems.

Experimental Research and Analysis of Selected Technological Parameters on the Roughness of Steel Area Surface HARDOX 500 with Thickness 40mm Cut by AWJ Technology

2013 ◽  
Vol 308 ◽  
pp. 13-17 ◽  
Author(s):  
Miloš Servátka ◽  
Stanislav Fabian
Keyword(s):  
Experimental Research ◽  
Manufacturing Systems ◽  
Manufacturing Processes ◽  
Large Set ◽  
Technological Parameters ◽  
New Findings ◽  
Technical University ◽  
Manufacturing Technologies

The paper presents a sample of wider set of new findings and recommendations from the area of the influence of technological parameters on the quality of surface of steel HARDOX 500 cut by AWJ technology and obtained by the evaluation of a large set of experiments within long-term activities of the Department of Manufacturing Processes Operation, Faculty of Manufacturing Technologies, Technical University in Košice, aimed at the diagnostics of operational states of manufacturing systems with AWJ technology.

Security System and Surveillance Using Real Time Object Tracking and Multiple Cameras

2011 ◽  
Vol 403-408 ◽  
pp. 4968-4973
Author(s):  
Rajendra Kachhava ◽  
Vivek Srivastava ◽  
Rajkumar Jain ◽  
Ekta Chaturvedi
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Real Time ◽  
Video Surveillance ◽  
Research Field ◽  
Security System ◽  
Multiple Cameras ◽  
Multiple Camera ◽  
Detection And Tracking ◽  
Real Time Tracking

In this paper we propose multiple cameras using real time tracking for surveillance and security system. It is extensively used in the research field of computer vision applications, like that video surveillance, authentication systems, robotics, pre-stage of MPEG4 image compression and user inter faces by gestures. The key components of tracking for surveillance system are extracting the feature, background subtraction and identification of extracted object. Video surveillance, object detection and tracking have drawn a successful increased interest in recent years. A object tracking can be understood as the problem of finding the path (i.e. trajectory) and it can be defined as a procedure to identify the different positions of the object in each frame of a video. Based on the previous work on single detection using single stationary camera, we extend the concept to enable the tracking of multiple object detection under multiple camera and also maintain a security based system by multiple camera to track person in indoor environment, to identify by my proposal system which consist of multiple camera to monitor a person. Present study mainly aims to provide security and detect the moving object in real time video sequences and live video streaming. Based on a robust algorithm for human body detection and tracking in videos created with support of multiple cameras.

Thinking Beyond Language: Intervention for Severe Aphasia

2009 ◽  
Vol 19 (1) ◽  
pp. 15-22 ◽  
Author(s):  
Nina Simmons-Mackie
Keyword(s):  
Quality Of Life ◽  
Communication Training ◽  
Language Intervention ◽  
Specific Training ◽  
Environmental Support ◽  
Total Communication ◽  
Communication Partners

Abstract Purpose: This article addresses several intervention approaches that aim to improve life for individuals with severe aphasia. Because severe aphasia significantly compromises language, often for the long term, recommended approaches focus on additional domains that affect quality of life. Treatments are discussed that involve increasing participation in personally relevant life situations, enhancing environmental support for communication and participation, and improving communicative confidence. Methods: Interventions that have been suggested in the aphasia literature as particularly appropriate for people with severe aphasia include training in total communication, training of communication partners, and activity specific training. Conclusion: Several intervention approaches can be implemented to enhance life with severe aphasia.

Communication Disordered: Program Implementation in Long-Term Care

2016 ◽  
Vol 1 (15) ◽  
pp. 64-67
Author(s):  
George Barnes ◽  
Joseph Salemi
Keyword(s):  
Program Implementation ◽  
Long Term Care ◽  
Family Satisfaction ◽  
Regulatory Compliance ◽  
Care Staff ◽  
Term Care ◽  
Staff Members ◽  
Change Policy

The organizational structure of long-term care (LTC) facilities often removes the rehab department from the interdisciplinary work culture, inhibiting the speech-language pathologist's (SLP's) communication with the facility administration and limiting the SLP's influence when implementing clinical programs. The SLP then is unable to change policy or monitor the actions of the care staff. When the SLP asks staff members to follow protocols not yet accepted by facility policy, staff may be unable to respond due to confusing or conflicting protocol. The SLP needs to involve members of the facility administration in the policy-making process in order to create successful clinical programs. The SLP must overcome communication barriers by understanding the needs of the administration to explain how staff compliance with clinical goals improves quality of care, regulatory compliance, and patient-family satisfaction, and has the potential to enhance revenue for the facility. By taking this approach, the SLP has a greater opportunity to increase safety, independence, and quality of life for patients who otherwise may not receive access to the appropriate services.

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