scholarly journals Siamese Tracking with Adaptive Template-Updating Strategy

2019 ◽  
Vol 9 (18) ◽  
pp. 3725 ◽  
Author(s):  
Zheng Xu ◽  
Haibo Luo ◽  
Bin Hui ◽  
Zheng Chang ◽  
Moran Ju
Keyword(s):  
State Of The Art ◽  
Contour Detection ◽  
Vehicle Tracking ◽  
Deformation Condition ◽  
Tracking Accuracy ◽  
Start Up ◽  
Tracking Process ◽  
Template Update ◽  
Network Experiment ◽  
Template Updating

Recently, we combined a contour-detection network and a fully convolutional Siamese tracking network to initialize a new start-up of vehicle tracking by clicking on the target, generating a contour proposal template instead of using a fixed bounding box. Tests on the OTB100 and Defense Advanced Research Projects Agency (DARPA) datasets proved that our method outperformed the state of the art and effectively solved the partial-occlusion problem. However, the current Siamese tracking method uses the target in the first frame as a template during the whole tracking period, and leads to the failed tracking of target deformation. In this paper, we propose a new template-update method and reconstruct the whole tracking process with a template-updating module. To be specific, the innovative adaptive template-updating module is comprised of a neural contour-detection network and a target-detection network. Experiment results on the DARPA dataset prove that our new tracking algorithm with the template-updating strategy prominently improved tracking accuracy regarding the deformation condition.

scholarly journals Siamese Tracking from Single Point Initialization

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 514 ◽  
Author(s):  
Zheng Xu ◽  
Haibo Luo ◽  
Bin Hui ◽  
Zheng Chang
Keyword(s):  
Object Tracking ◽  
State Of The Art ◽  
Single Point ◽  
Contour Detection ◽  
Tracking Accuracy ◽  
Object Contour ◽  
Start Up ◽  
Tracking Process ◽  
Real Time Tracking ◽  
Target Locating

Recently, we have been concerned with locating and tracking vehicles in aerial videos. Vehicles in aerial videos usually have small sizes due to use of cameras from a remote distance. However, most of the current methods use a fixed bounding box region as the input of tracking. For the purpose of target locating and tracking in our system, detecting the contour of the target is utilized and can help with improving the accuracy of target tracking, because a shape-adaptive template segmented by object contour contains the most useful information and the least background for object tracking. In this paper, we propose a new start-up of tracking by clicking on the target, and implement the whole tracking process by modifying and combining a contour detection network and a fully convolutional Siamese tracking network. The experimental results show that our algorithm has significantly improved tracking accuracy compared to the state-of-the-art regarding vehicle images in both OTB100 and DARPA datasets. We propose utilizing our method in real time tracking and guidance systems.

scholarly journals Least Trimmed Squares Approach to Lucas-Kanade Algorithm in Object Tracking Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Yih-Lon Lin
Keyword(s):  
Cost Function ◽  
Object Tracking ◽  
Vehicle Tracking ◽  
Tracking Accuracy ◽  
Least Trimmed Squares ◽  
Environment Adaptation ◽  
Tracking Process ◽  
Important Research Topic ◽  
Process Simulations ◽  
The Cost

The object tracking problem is an important research topic in computer vision. For real applications such as vehicle tracking and face tracking, there are many efficient and real-time algorithms. In this study, we will focus on the Lucas-Kanade (LK) algorithm for object tracking. Although this method is time consuming, it is effective in tracking accuracy and environment adaptation. In the standard LK method, the sum of squared errors is used as the cost function, while least trimmed squares is adopted as the cost function in this study. The resulting estimator is robust against outliers caused by noises and occlusions in the tracking process. Simulations are provided to show that the proposed algorithm outperforms the standard LK method in the sense that it is robust against the outliers in the object tracking problems.

scholarly journals Learning the Incremental Warp for 3D Vehicle Tracking in LiDAR Point Clouds

2021 ◽  
Vol 13 (14) ◽  
pp. 2770
Author(s):  
Shengjing Tian ◽  
Xiuping Liu ◽  
Meng Liu ◽  
Yuhao Bian ◽  
Junbin Gao ◽  
...  
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Vehicle Tracking ◽  
Complex Nature ◽  
Global Feature ◽  
Tracking Accuracy ◽  
State Estimator ◽  
Latent Space ◽  
3D Shapes ◽  
Cloud Tracking

Object tracking from LiDAR point clouds, which are always incomplete, sparse, and unstructured, plays a crucial role in urban navigation. Some existing methods utilize a learned similarity network for locating the target, immensely limiting the advancements in tracking accuracy. In this study, we leveraged a powerful target discriminator and an accurate state estimator to robustly track target objects in challenging point cloud scenarios. Considering the complex nature of estimating the state, we extended the traditional Lucas and Kanade (LK) algorithm to 3D point cloud tracking. Specifically, we propose a state estimation subnetwork that aims to learn the incremental warp for updating the coarse target state. Moreover, to obtain a coarse state, we present a simple yet efficient discrimination subnetwork. It can project 3D shapes into a more discriminatory latent space by integrating the global feature into each point-wise feature. Experiments on KITTI and PandaSet datasets showed that compared with the most advanced of other methods, our proposed method can achieve significant improvements—in particular, up to 13.68% on KITTI.

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.

scholarly journals Contour Detection for Fibre of Preserved Szechuan Pickle Based on Dilated Convolution

2019 ◽  
Vol 9 (13) ◽  
pp. 2684 ◽  
Author(s):  
Hongyang Li ◽  
Lizhuang Liu ◽  
Zhenqi Han ◽  
Dan Zhao
Keyword(s):  
Edge Detection ◽  
State Of The Art ◽  
Contour Detection ◽  
Experimental Results ◽  
Contour Method ◽  
Class Differences ◽  
Dilated Convolution ◽  
The Mean ◽  
Art Performance

Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automatic peeling device, is studied. The fibre contour is a kind of non-salient contour, characterized by big intra-class differences and small inter-class differences, meaning that the feature of the contour is not discriminative. The method called dilated-holistically-nested edge detection (Dilated-HED) is proposed to detect the fibre contour, which is built based on the HED network and dilated convolution. The experimental results for our dataset show that the Pixel Accuracy (PA) is 99.52% and the Mean Intersection over Union (MIoU) is 49.99%, achieving state-of-the-art performance.

scholarly journals Mobile Blended Learning with eSquirrel

2015 ◽  
Vol 8 (4Β) ◽  
Author(s):  
Michael Maurer
Keyword(s):  
Blended Learning ◽  
Mobile Learning ◽  
Recent Progress ◽  
State Of The Art ◽  
The State ◽  
Educational Institutions ◽  
Classroom Teaching ◽  
Learning Platform ◽  
Start Up ◽  
Innovative Solution

This article outlines the state of the art of mobile blended learning apps. It describes recent progress in this area, and explains the potential of mobile blended learning for schools and educational institutions. Furthermore, it presents an innovative solution, eSquirrel, which is developed by an Austrian inter-disciplinary start-up. eSquirrel is a blended learning platform that combines mobile learning with gamification. It blends the concepts of classroom teaching, eLearning and learning from books into a native Android and iOS course app, and enables teachers to learn their students’ progress.

scholarly journals Granulated zeolite plant "Alusil", Zvornik

2003 ◽  
Vol 57 (2) ◽  
pp. 51-53
Author(s):  
Mirjana Stankovic ◽  
Lato Pezo
Keyword(s):  
Production Process ◽  
Pilot Plant ◽  
State Of The Art ◽  
High Tech ◽  
Production Plant ◽  
Start Up ◽  
Engineering Department ◽  
Production Complex ◽  
Initial Capacity ◽  
Zeolite Production

The IGPC's Engineering Department designed basic technological and machine projects for a granulated zeolite production plant, on the basis of which a pilot plant with an initial capacity of 5,000 t/y was constructed in 1984, within Birac - Zvornik production complex. The technology in these projects was developed in the laboratories of the IGPC.Several goals were realized by designing a granulated zeolite production plant. This technology is one of the newest state of the art high tech technologies. The product meets all quality demands, as well as environmental regulations, by which granulated zeolite production for various uses was developed. The granulated zeolite production process is fully automatized, and the product has uniform quality. There is no waste material in granulated zeolite production, because all products with unsatisfactory quality are returned to the process. The production process can be controlled manually, which is necessary during start - up, and repairs.

scholarly journals Research on Target Tracking Algorithm Based on Siamese Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haibo Pang ◽  
Qi Xuan ◽  
Meiqin Xie ◽  
Chengming Liu ◽  
Zhanbo Li
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Evaluation Criteria ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Temporal Attention ◽  
Tracking Accuracy ◽  
Current Frame ◽  
Tracking Process ◽  
Recognition Ability

Target tracking is a significant topic in the field of computer vision. In this paper, the target tracking algorithm based on deep Siamese network is studied. Aiming at the situation that the tracking process is not robust, such as drift or miss the target, the tracking accuracy and robustness of the algorithm are improved by improving the feature extraction part and online update part. This paper adds SE-block and temporal attention mechanism (TAM) to the framework of Siamese neural network. SE-block can refine and extract features; different channels are given different weights according to their importance which can improve the discrimination of the network and the recognition ability of the tracker. Temporal attention mechanism can update the target state by adjusting the weights of samples at current frame and historical frame to solve the model drift caused by the existence of similar background. We use cross-entropy loss to distinguish the targets in different sequences so that their distance in the feature domains is longer and the features are easier to identify. We train and test the network on three benchmarks and compare with several state-of-the-art tracking methods. The experimental results demonstrate that the algorithm proposed is superior to other methods in tracking effect diagram and evaluation criteria. The proposed algorithm can solve the occlusion problem effectively while ensuring the real-time performance in the process of tracking.

scholarly journals INTERACTIVE CADASTRAL BOUNDARY DELINEATION FROM UAV DATA

2018 ◽  
Vol IV-2 ◽  
pp. 81-88 ◽  
Author(s):  
S. Crommelinck ◽  
B. Höfle ◽  
M. N. Koeva ◽  
M. Y. Yang ◽  
G. Vosselman
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Land Tenure ◽  
State Of The Art ◽  
Cost Effective ◽  
Contour Detection ◽  
Local Boundary ◽  
Manual Delineation ◽  
Cost Paths ◽  
Cadastral Surveying ◽  
Least Cost Paths

Unmanned aerial vehicles (UAV) are evolving as an alternative tool to acquire land tenure data. UAVs can capture geospatial data at high quality and resolution in a cost-effective, transparent and flexible manner, from which visible land parcel boundaries, i.e., cadastral boundaries are delineable. This delineation is to no extent automated, even though physical objects automatically retrievable through image analysis methods mark a large portion of cadastral boundaries. This study proposes (i) a methodology that automatically extracts and processes candidate cadastral boundary features from UAV data, and (ii) a procedure for a subsequent interactive delineation. Part (i) consists of two state-of-the-art computer vision methods, namely gPb contour detection and SLIC superpixels, as well as a classification part assigning costs to each outline according to local boundary knowledge. Part (ii) allows a user-guided delineation by calculating least-cost paths along previously extracted and weighted lines. The approach is tested on visible road outlines in two UAV datasets from Germany. Results show that all roads can be delineated comprehensively. Compared to manual delineation, the number of clicks per 100 m is reduced by up to 86 %, while obtaining a similar localization quality. The approach shows promising results to reduce the effort of manual delineation that is currently employed for indirect (cadastral) surveying.

Research on Underwater Target Tracking Based on Contour Detection

2011 ◽  
Vol 317-319 ◽  
pp. 890-896
Author(s):  
Ming Jun Zhang ◽  
Yuan Yuan Wan ◽  
Zhen Zhong Chu
Keyword(s):  
Prediction Model ◽  
Target Tracking ◽  
Tracking System ◽  
Contour Detection ◽  
Motion Prediction ◽  
Moving Target ◽  
Contour Extraction ◽  
Tracking Accuracy ◽  
Tracking Method ◽  
Underwater Target

The traditional centroid tracking method over-relies on the accuracy of segment, which easily lead to loss of underwater moving target. This paper presents an object tracking method based on circular contour extraction, combining region feature and contour feature. Through the correction to circle features, the problem of multiple solutions causing by Hough transform circle detection is avoided. A new motion prediction model is constructed to make up the deficiency that three-order motion prediction model has disadvantage of high dimension and large calculation. The predicted position of object centroid is updated and corrected by circle contour, forming prediction-measurement-updating closed-loop target tracking system. To reduce system processing time, on the premise of the tracking accuracy, a dynamic detection method based on target state prediction model is proposed. The results of contour extraction and underwater moving target experiments demonstrate the effectiveness of the proposed method.

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