scholarly journals Object Tracking via 2DPCA andl2-Regularization

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Haijun Wang ◽  
Hongjuan Ge ◽  
Shengyan Zhang
Keyword(s):  
Bayesian Inference ◽  
Object Tracking ◽  
Likelihood Function ◽  
State Of The Art ◽  
Reconstruction Error ◽  
Image Sequences ◽  
Projection Matrix ◽  
Tracking Method ◽  
Fast Tracking ◽  
Bayesian Inference Framework

We present a fast and robust object tracking algorithm by using 2DPCA andl2-regularization in a Bayesian inference framework. Firstly, we model the challenging appearance of the tracked object using 2DPCA bases, which exploit the strength of subspace representation. Secondly, we adopt thel2-regularization to solve the proposed presentation model and remove the trivial templates from the sparse tracking method which can provide a more fast tracking performance. Finally, we present a novel likelihood function that considers the reconstruction error, which is concluded from the orthogonal left-projection matrix and the orthogonal right-projection matrix. Experimental results on several challenging image sequences demonstrate that the proposed method can achieve more favorable performance against state-of-the-art tracking algorithms.

ROBUST AND FAST TRACKING ALGORITHM IN VIDEO SEQUENCES BY ADAPTIVE WINDOW SIZING USING A NOVEL ANALYSIS ON SPATIOTEMPORAL GRADIENT POWERS

2007 ◽  
Vol 16 (02) ◽  
pp. 305-317 ◽  
Author(s):  
PAYMAN MOALLEM ◽  
ALIREZA MEMARMOGHADDAM ◽  
MOHSEN ASHOURIAN
Keyword(s):  
Window Size ◽  
Image Sequences ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Occupancy Rate ◽  
Tracking Method ◽  
Fast Tracking ◽  
Adaptive Window ◽  
Real Video ◽  
Adaptively Adjusting

Success of a tracking method depends largely on choosing the suitable window size as soon as the target size changes in image sequences. To achieve this goal, we propose a fast tracking algorithm based on adaptively adjusting tracking window. Firstly, tracking window is divided into four edge subwindows, and a background subwindow around it. Then, by calculating the spatiotemporal gradient power ratios of the target in each subwindow, four proper expansion vectors are associated with any tracking window sides such that the occupancy rate of the target in tracking window should be maintained within a specified range. In addition, since temporal changing of target is evaluated in calculating these vectors, we estimate overall target displacement by sum of expansion vectors. Experimental results using various real video sequences show that the proposed algorithm successfully track an unknown textured target in real time, and is robust to dynamic occlusions in complex noisy backgrounds.

scholarly journals Online Multi-Object Tracking in Videos Based on Features Detected by YOLO

2021 ◽  
Vol 12 (6) ◽  
pp. 2922-2931
Author(s):  
Younis A. Al-Arbo, Prof.Dr. Khalil I. Alsaif
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Tracking ◽  
State Of The Art ◽  
Rapid Development ◽  
Tracking Accuracy ◽  
Tracking Method ◽  
Detection And Tracking ◽  
Objects Detection ◽  
Significant Attention

With the rapid development of different applications that rely on multi-object detection and tracking, significant attention has been brought toward improving the performance of these methods. Recently, Artificial Neural Networks (ANNs) have shown outstanding performance in different applications, where objects detection and tracking are no exception. In this paper, we proposed a new object tracking method based on descriptors extracted using the convolutional filters of the YOLOv3 neural network. As these features are detected and processed during the detection phase, the proposed method has exploited these features to produce efficient and robust descriptors. The proposed method has shown better performance, compared to state-of-the-art methods, by producing better predictions using less computations. The evaluation results show that the proposed method has been able to process an average of 207.6 frames per second to track objects with 67.6% Multi-Object Tracking Accuracy (MOTA) and 89.1% Multi-Object Tracking Precision (MOTP).

scholarly journals Adding Constraints to Bayesian Inverse Problems

2019 ◽  
Vol 33 ◽  
pp. 1666-1673 ◽  
Author(s):  
Jiacheng Wu ◽  
Jian-Xun Wang ◽  
Shawn C. Shadden
Keyword(s):  
Kalman Filter ◽  
Bayesian Inference ◽  
Inverse Problems ◽  
Ensemble Kalman Filter ◽  
Computational Models ◽  
Likelihood Function ◽  
Filter Method ◽  
Observation Data ◽  
True Parameter ◽  
Bayesian Inference Framework

Using observation data to estimate unknown parameters in computational models is broadly important. This task is often challenging because solutions are non-unique due to the complexity of the model and limited observation data. However, the parameters or states of the model are often known to satisfy additional constraints beyond the model. Thus, we propose an approach to improve parameter estimation in such inverse problems by incorporating constraints in a Bayesian inference framework. Constraints are imposed by constructing a likelihood function based on fitness of the solution to the constraints. The posterior distribution of the parameters conditioned on (1) the observed data and (2) satisfaction of the constraints is obtained, and the estimate of the parameters is given by the maximum a posteriori estimation or posterior mean. Both equality and inequality constraints can be considered by this framework, and the strictness of the constraints can be controlled by constraint uncertainty denoting a confidence on its correctness. Furthermore, we extend this framework to an approximate Bayesian inference framework in terms of the ensemble Kalman filter method, where the constraint is imposed by re-weighing the ensemble members based on the likelihood function. A synthetic model is presented to demonstrate the effectiveness of the proposed method and in both the exact Bayesian inference and ensemble Kalman filter scenarios, numerical simulations show that imposing constraints using the method presented improves identification of the true parameter solution among multiple local minima.

Probabilistic Background Model by Density Forests for Tracking

2017 ◽  
Vol 5 (2) ◽  
pp. 1-16
Author(s):  
Daimu Oiwa ◽  
Shinji Fukui ◽  
Yuji Iwahori ◽  
Tsuyoshi Nakamura ◽  
Boonserm Kijsirikul ◽  
...  
Keyword(s):  
Likelihood Function ◽  
Target Object ◽  
Image Sequences ◽  
Background Model ◽  
Robust Tracking ◽  
Color Information ◽  
Background Region ◽  
Previous Approach ◽  
Tracking Method ◽  
Outdoor Scenes

This paper proposes an approach for a robust tracking method to the objects intersection with appearances similar to a target object. The target is image sequences taken by a moving camera in this paper. Tracking methods using color information tend to track mistakenly a background region or an object with color similar to the target object since the proposed method is based on the particle filter. The method constructs the probabilistic background model by the histogram of the optical flow and defines the likelihood function so that the likelihood in the region of the target object may become large. This leads to increasing the accuracy of tracking. The probabilistic background model is made by the density forests. It can infer a probabilistic density fast. The proposed method can process faster than the authors' previous approach by introducing the density forests. Results are demonstrated by experiments using the real videos of outdoor scenes.

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

Multi-object Tracking Method Based on Efficient Channel Attention and Switchable Atrous Convolution

2021 ◽  
Author(s):  
Xuezhi Xiang ◽  
Wenkai Ren ◽  
Yujian Qiu ◽  
Kaixu Zhang ◽  
Ning Lv
Keyword(s):  
Object Tracking ◽  
Tracking Method

Multi-Scale Anti-Occlusion Correlation Filters Object Tracking Method Based on Complementary Features

2020 ◽  
pp. 2155002
Author(s):  
Xiuhua Hu ◽  
Yuan Chen ◽  
Yan Hui ◽  
Yingyu Liang ◽  
Guiping Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Scale Model ◽  
Correlation Filters ◽  
Motion Feature ◽  
Tracking Method ◽  
Object State ◽  
Multi Scale ◽  
Tracking Process ◽  
Object Appearance ◽  
Response Peak

Aiming to tackle the problem of tracking drift easily caused by complex factors during the tracking process, this paper proposes an improved object tracking method under the framework of kernel correlation filter. To achieve discriminative information that is not sensitive to object appearance change, it combines dimensionality-reduced Histogram of Oriented Gradients features and Lab color features, which can be used to exploit the complementary characteristics robustly. Based on the idea of multi-resolution pyramid theory, a multi-scale model of the object is constructed, and the optimal scale for tracking the object is found according to the confidence maps’ response peaks of different sizes. For the case that tracking failure can easily occur when there exists inappropriate updating in the model, it detects occlusion based on whether the occlusion rate of the response peak corresponding to the best object state is less than a set threshold. At the same time, Kalman filter is used to record the motion feature information of the object before occlusion, and predict the state of the object disturbed by occlusion, which can achieve robust tracking of the object affected by occlusion influence. Experimental results show the effectiveness of the proposed method in handling various internal and external interferences under challenging environments.

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