scholarly journals Patch Based Multiple Instance Learning Algorithm for Object Tracking

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zhenjie Wang ◽  
Lijia Wang ◽  
Hua Zhang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Partial Occlusion ◽  
Learning Rates ◽  
Classification Score ◽  
Object Based ◽  
Illumination Changes

To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed. The algorithm divides an object into many blocks. Then, the online MIL algorithm is applied on each block for obtaining strong classifier. The algorithm takes account of both the average classification score and classification scores of all the blocks for detecting the object. In particular, compared with the whole object based MIL algorithm, the P-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs. After detecting the object, the learning rates for updating weak classifiers’ parameters are adaptively tuned. The classifier updating strategy avoids overupdating and underupdating the parameters. Finally, the proposed method is compared with other state-of-the-art algorithms on several classical videos. The experiment results illustrate that the proposed method performs well especially in case of illumination changes or pose variations and partial occlusion. Moreover, the algorithm realizes real-time object tracking.

scholarly journals Real-time gaze estimation via pupil center tracking

2018 ◽  
Vol 9 (1) ◽  
pp. 6-18 ◽  
Author(s):  
Dario Cazzato ◽  
Fabio Dominio ◽  
Roberto Manduchi ◽  
Silvia M. Castro
Keyword(s):  
Real Time ◽  
Learning Algorithm ◽  
Natural Environments ◽  
Gaze Estimation ◽  
Head Pose ◽  
Data Set ◽  
Gaze Tracking ◽  
Illumination Changes ◽  
Wide Range ◽  
Estimation System

Abstract Automatic gaze estimation not based on commercial and expensive eye tracking hardware solutions can enable several applications in the fields of human computer interaction (HCI) and human behavior analysis. It is therefore not surprising that several related techniques and methods have been investigated in recent years. However, very few camera-based systems proposed in the literature are both real-time and robust. In this work, we propose a real-time user-calibration-free gaze estimation system that does not need person-dependent calibration, can deal with illumination changes and head pose variations, and can work with a wide range of distances from the camera. Our solution is based on a 3-D appearance-based method that processes the images from a built-in laptop camera. Real-time performance is obtained by combining head pose information with geometrical eye features to train a machine learning algorithm. Our method has been validated on a data set of images of users in natural environments, and shows promising results. The possibility of a real-time implementation, combined with the good quality of gaze tracking, make this system suitable for various HCI applications.

IMPLEMENTING BACK-PROPAGATION- THROUGH-TIME LEARNING ALGORITHM USING CELLULAR NEURAL NETWORKS

1999 ◽  
Vol 09 (06) ◽  
pp. 1041-1074 ◽  
Author(s):  
TAO YANG ◽  
LEON O. CHUA
Keyword(s):  
Real Time ◽  
Time Complexity ◽  
Large Scale ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Back Propagation ◽  
Cellular Neural Network ◽  
Small Scale ◽  
Von Neumann ◽  
On Line

In a programmable (multistage) cellular neural network (CNN) structure, the CPU is a CNN universal chip which supports massively parallel computations on patterns and images, including videos. In this paper, we decompose the structure of a class of simultaneous recurrent networks (SRN) into a CNN program and run it on a von Neumann-like stored program CNN structure. To train the SRN, we map the back-propagation-through-time (BTT) learning algorithm into a sequence of CNN subroutines to achieve real-time performance via a CNN universal chip. By computing in parallel, the CNN universal chip can be programmed to implement in real time the BTT learning algorithm, which has a very high time complexity. An estimate of the time complexity of the BTT learning algorithm based on the CNN universal chip is presented. For small-scale problems, our simulation results show that a CNN implementation of the BTT learning algorithm for a two-dimensional SRN is at least 10,000 times faster than that based on state-of-the-art sequential workstations. For the few large-scale problems which we have so far simulated, the CNN implemented BTT learning algorithm maintained virtually the same time complexity with a learning time of a few seconds, while those implemented on state-of-the-art sequential workstations dramatically increased their time complexity, often requiring several days of running time. Several examples are presented to demonstrate how efficiently a CNN universal chip can speed up the learning algorithm for both off-line and on-line applications.

scholarly journals Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning

2020 ◽  
Vol 34 (07) ◽  
pp. 11205-11212 ◽  
Author(s):  
Ilchae Jung ◽  
Kihyun You ◽  
Hyeonwoo Noh ◽  
Minsu Cho ◽  
Bohyung Han
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Ground Truth ◽  
Learning Task ◽  
Model Parameters ◽  
Model Adaptation ◽  
Learning Framework ◽  
Learning Rates ◽  
Meta Learning ◽  
The Cost

We propose a novel meta-learning framework for real-time object tracking with efficient model adaptation and channel pruning. Given an object tracker, our framework learns to fine-tune its model parameters in only a few gradient-descent iterations during tracking while pruning its network channels using the target ground-truth at the first frame. Such a learning problem is formulated as a meta-learning task, where a meta-tracker is trained by updating its meta-parameters for initial weights, learning rates, and pruning masks through carefully designed tracking simulations. The integrated meta-tracker greatly improves tracking performance by accelerating the convergence of online learning and reducing the cost of feature computation. Experimental evaluation on the standard datasets demonstrates its outstanding accuracy and speed compared to the state-of-the-art methods.

scholarly journals Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 650
Author(s):  
B. M. Ruhul Amin ◽  
M. J. Hossain ◽  
Adnan Anwar ◽  
Shafquat Zaman
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Detection Rate ◽  
Electronic Device ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Research Work ◽  
Load Flow ◽  
Cyber Attacks ◽  
Real Time System

Intelligent electronic devices (IEDs) along with advanced information and communication technology (ICT)-based networks are emerging in the legacy power grid to obtain real-time system states and provide the energy management system (EMS) with wide-area monitoring and advanced control capabilities. Cyber attackers can inject malicious data into the EMS to mislead the state estimation process and disrupt operations or initiate blackouts. A machine learning algorithm (MLA)-based approach is presented in this paper to detect false data injection attacks (FDIAs) in an IED-based EMS. In addition, stealthy construction of FDIAs and their impact on the detection rate of MLAs are analyzed. Furthermore, the impacts of natural disturbances such as faults on the system are considered, and the research work is extended to distinguish between cyber attacks and faults by using state-of-the-art MLAs. In this paper, state-of-the-art MLAs such as Random Forest, OneR, Naive Bayes, SVM, and AdaBoost are used as detection classifiers, and performance parameters such as detection rate, false positive rate, precision, recall, and f-measure are analyzed for different case scenarios on the IEEE benchmark 14-bus system. The experimental results are validated using real-time load flow data from the New York Independent System Operator (NYISO).

Real-time object tracking via online weighted multiple instance learning

2014 ◽  
Vol 22 (6) ◽  
pp. 1661-1667 ◽  
Author(s):  
CHEN Dong-cheng ◽  
ZHU Ming ◽  
GAO Wen ◽  
SUN Hong-hai ◽  
YANG Wen-bo
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Multiple Instance Learning

scholarly journals Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Li Jia Wang ◽  
Hua Zhang
Keyword(s):  
Visual Tracking ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Tracking Algorithm ◽  
Inner Product ◽  
Selection Strategy ◽  
Weak Classifier ◽  
Update Strategy ◽  
Appearance Changes

An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose weak classifier from a classifier pool, which avoids computing instance probabilities and bag probabilityMtimes. Furthermore, a feedback strategy is presented to update weak classifiers. In the feedback update strategy, different weights are assigned to the tracking result and template according to the maximum classifier score. Finally, the presented algorithm is compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed tracking algorithm runs in real-time and is robust to occlusion and appearance changes.

scholarly journals A YOLO based Technique for Early Forest Fire Detection

2020 ◽  
Vol 9 (4) ◽  
pp. 1357-1362
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Forest Fire ◽  
Forest Fires ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Mobile App ◽  
Raspberry Pi ◽  
Time Data ◽  
Alert Message

Forest fires, wildfires and bushfires are a global environmental problem that causes serious damage each year. The most significant factors in the fight against forest fires involve earliest possible detection of the fire, flame or smoke event, proper classification of the fire and rapid response from the fire departments. In this paper, we developed an automatic early warning system that incorporates multiple sensors and state of the art deep learning algorithm which has a minimum number of false positives and give a good accuracy in real time data and in the lowest cost possible to our drone to monitor forest fire as early as possible and report it to the concerned authority. The drones will be equipped with sensors, Raspberry pi 3, neural stick, APM 2.5, GPS, Wifi. The neural stick will be used for real time image processing using our state-of-the-art deep learning model. And as soon as forest fire is detected the UAV will send an alert message to the concerned authority on the mobile App along with location coordinates of the fire, image and the amount of area in which forest is spread using a mesh messaging. So that immediate action will be taken to stop it from spreading and causing loss of millions of lives and money. Using both deep learning and infrared cameras to monitor the forest and surrounding area, we will take advantage of recent advances in multi-sensor surveillance technologies. This innovative technique helps the forest department to detect fire in first 12 hours of its initialization , which is the most effective time to control the fire.

scholarly journals Partial Block Scheme and Adaptive Update Model for Kernelized Correlation Filters-Based Object Tracking

2018 ◽  
Vol 8 (8) ◽  
pp. 1349 ◽  
Author(s):  
Soowoong Jeong ◽  
Joonki Paik
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Adaptive Learning ◽  
Weighting Function ◽  
Computational Cost ◽  
Visual Object ◽  
Partial Occlusion ◽  
Block Scheme ◽  
Real Time Tracking ◽  
Scale Variation

In visual object tracking, the dynamic environment is a challenging issue. Partial occlusion and scale variation are typical challenging problems. We present a correlation-based object tracking based on the discriminative model. To attenuate the influence by partial occlusion, partial sub-blocks are constructed from the original block, and each of them operates independently. The scale space is employed to deal with scale variation using a feature pyramid. We also present an adaptive update model with a weighting function to calculate the frame-adaptive learning rate. Theoretical analysis and experimental results demonstrate that the proposed method can robustly track drastic deformed objects. The sparse update reduces the computational cost for real-time tracking. Although the partial block scheme generation increases the computational cost, we present a novel sparse update approach to reduce the computational cost drastically for real-time tracking. The experiments were performed on a variety of sequences, and the proposed method exhibited better performance compared with the state-of-the-art trackers.

Reverse Validation Based Adaptive Particle Filter Algorithm for Object Tracking

2014 ◽  
Vol 1044-1045 ◽  
pp. 1302-1308
Author(s):  
Shao Mei Li ◽  
Kai Wang ◽  
Chao Gao ◽  
Ya Wen Wang
Keyword(s):  
Object Tracking ◽  
Particle Filtering ◽  
State Of The Art ◽  
Appearance Model ◽  
Object Based ◽  
Object Appearance ◽  
Sift Features ◽  
Particle Filter Algorithm ◽  
Tracking Object ◽  
Object Frame

To improves tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, tracking object frame by frame via color histogram and particle filtering. Secondly, reversely validating the tracking result based on particle filtering. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm can not only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences.

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