scholarly journals Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7888
Author(s):  
Li-Yu Lo ◽  
Chi Hao Yiu ◽  
Yu Tang ◽  
An-Shik Yang ◽  
Boyang Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Vision System ◽  
Estimation Method ◽  
Target Object ◽  
Research Community ◽  
Quadrotor Uav ◽  
Real World Applications ◽  
Object Pose Estimation ◽  
Semantic Object ◽  
Surveillance Applications

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, a UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman filter to enhance the perception performance. In addition, UAV path planning for a surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.

scholarly journals Particle Filter with Gaussian Weighting for Vehicle Tracking

2011 ◽  
Vol 15 (6) ◽  
pp. 681-686
Author(s):  
Indah Agustien Siradjuddin ◽  
◽  
Muhammad Rahmat Widyanto ◽  
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Gaussian Distribution ◽  
Likelihood Function ◽  
Target Object ◽  
Video Data ◽  
Vehicle Tracking ◽  
Computational Time ◽  
Filter Method ◽  
Vehicle Motion

To track vehicle motion in data video, particle filter with Gaussian weighting is proposed. This method consists of four main stages. First, particles are generated to predict target’s location. Second, certain particles are searched and these particles are used to build Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, particles are updated based on each weight. The proposed method could reduce computational time of tracking compared to that of conventional method of particle filter, since the proposed method does not have to calculate all particles weight using likelihood function. This method has been tested on video data with car as a target object. In average, this proposed method of particle filter is 60.61% times faster than particle filter method meanwhile the accuracy of tracking with this newmethod is comparable with particle filter method, which reach up to 86.87%. Hence this method is promising for real time object tracking application.

scholarly journals Learning-Type Anchors-Driven Pose Estimation for the Autolanding Fixed-Wing UAVs

2021 ◽  
Author(s):  
Dengqing Tang ◽  
Lincheng Shen ◽  
Xiaojiao Xiang ◽  
Han Zhou ◽  
Tianjiang Hu
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Stereo Vision ◽  
Vision System ◽  
Estimation Method ◽  
Satellite System ◽  
Training Dataset ◽  
Estimation Accuracy ◽  
The Impact ◽  
Outdoor Experiments

<p>We propose a learning-type anchors-driven real-time pose estimation method for the autolanding fixed-wing unmanned aerial vehicle (UAV). The proposed method enables online tracking of both position and attitude by the ground stereo vision system in the Global Navigation Satellite System denied environments. A pipeline of convolutional neural network (CNN)-based UAV anchors detection and anchors-driven UAV pose estimation are employed. To realize robust and accurate anchors detection, we design and implement a Block-CNN architecture to reduce the impact of the outliers. With the basis of the anchors, monocular and stereo vision-based filters are established to update the UAV position and attitude. To expand the training dataset without extra outdoor experiments, we develop a parallel system containing the outdoor and simulated systems with the same configuration. Simulated and outdoor experiments are performed to demonstrate the remarkable pose estimation accuracy improvement compared with the conventional Perspective-N-Points solution. In addition, the experiments also validate the feasibility of the proposed architecture and algorithm in terms of the accuracy and real-time capability requirements for fixed-wing autolanding UAVs.</p>

Complementary Object Tracking Using Average Peak-to-Correlation Energy

2021 ◽  
Author(s):  
Kosuke Honda ◽  
Hamido Fujita
Keyword(s):  
Neural Networks ◽  
Object Tracking ◽  
Convolutional Neural Networks ◽  
Correlation Energy ◽  
Target Object ◽  
The Other ◽  
Tracking Performance ◽  
Correlation Filter ◽  
Evaluation Index ◽  
Siamese Network

In recent years, template-based methods such as Siamese network trackers and Correlation Filter (CF) based trackers have achieved state-of-the-art performance in several benchmarks. Recent Siamese network trackers use deep features extracted from convolutional neural networks to locate the target. However, the tracking performance of these trackers decreases when there are similar distractors to the object and the target object is deformed. On the other hand, correlation filter (CF)-based trackers that use handcrafted features (e.g., HOG features) to spatially locate the target. These two approaches have complementary characteristics due to differences in learning methods, features used, and the size of search regions. Also, we found that these trackers are complementary in terms of performance in benchmarking. Therefore, we propose the “Complementary Tracking framework using Average peak-to-correlation energy” (CTA). CTA is the generic object tracking framework that connects CF-trackers and Siamese-trackers in parallel and exploits the complementary features of these. In CTA, when a tracking failure of the Siamese tracker is detected using Average peak-to-correlation energy (APCE), which is an evaluation index of the response map matrix, the CF-trackers correct the output. In experimental on OTB100, CTA significantly improves the performance over the original tracker for several combinations of Siamese-trackers and CF-rackers.

scholarly journals A Methodology for Multi-Camera Surface-Shape Estimation of Deformable Unknown Objects

Robotics ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 69 ◽  
Author(s):  
Evgeny Nuger ◽  
Beno Benhabib
Keyword(s):  
Particle Filtering ◽  
Surface Deformation ◽  
Vision System ◽  
Three Dimensional ◽  
Target Object ◽  
Surface Shape ◽  
Shape Estimation ◽  
Formal Approach ◽  
Camera System ◽  
Unknown Objects

A novel methodology is proposed herein to estimate the three-dimensional (3D) surface shape of unknown, markerless deforming objects through a modular multi-camera vision system. The methodology is a generalized formal approach to shape estimation for a priori unknown objects. Accurate shape estimation is accomplished through a robust, adaptive particle filtering process. The estimation process yields a set of surface meshes representing the expected deformation of the target object. The methodology is based on the use of a multi-camera system, with a variable number of cameras, and range of object motions. The numerous simulations and experiments presented herein demonstrate the proposed methodology’s ability to accurately estimate the surface deformation of unknown objects, as well as its robustness to object loss under self-occlusion, and varying motion dynamics.

scholarly journals Monocular Robust Depth Estimation Vision System for Robotic Tasks Interventions in Metallic Targets

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3220 ◽  
Author(s):  
Carlos Veiga Almagro ◽  
Mario Di Castro ◽  
Giacomo Lunghi ◽  
Raúl Marín Prades ◽  
Pedro José Sanz Valero ◽  
...  
Keyword(s):  
Object Tracking ◽  
Vision System ◽  
Nuclear Research ◽  
Depth Estimation ◽  
Work Load ◽  
Manual Control ◽  
Repetitive Tasks ◽  
Autonomous Mode ◽  
Robotic Tasks ◽  
Tracking And Recognition

Robotic interventions in hazardous scenarios need to pay special attention to safety, as in most cases it is necessary to have an expert operator in the loop. Moreover, the use of a multi-modal Human-Robot Interface allows the user to interact with the robot using manual control in critical steps, as well as semi-autonomous behaviours in more secure scenarios, by using, for example, object tracking and recognition techniques. This paper describes a novel vision system to track and estimate the depth of metallic targets for robotic interventions. The system has been designed for on-hand monocular cameras, focusing on solving lack of visibility and partial occlusions. This solution has been validated during real interventions at the Centre for Nuclear Research (CERN) accelerator facilities, achieving 95% success in autonomous mode and 100% in a supervised manner. The system increases the safety and efficiency of the robotic operations, reducing the cognitive fatigue of the operator during non-critical mission phases. The integration of such an assistance system is especially important when facing complex (or repetitive) tasks, in order to reduce the work load and accumulated stress of the operator, enhancing the performance and safety of the mission.

A Method to Solve the Side Effects of Dual-Line Timed Address Event Vision System

2015 ◽  
Vol 24 (03) ◽  
pp. 1550028 ◽  
Author(s):  
Jiangtao Xu ◽  
Mengxing Zhang ◽  
Shi Yan ◽  
Suying Yao
Keyword(s):  
Side Effects ◽  
Vision System ◽  
Vertical Axis ◽  
Behavioral Model ◽  
Target Object ◽  
Intensity Difference ◽  
Time Process ◽  
Vision Sensor ◽  
Motion Direction ◽  
Order Of Magnitude

A method to reduce the side effects of dual-line timed address-event (TAE) vision system is proposed in this paper. The side effects include edge discontinuity and the natural insensitivity to object edges in the motion direction. X-event, a kind of artificial event is introduced to represent light intensity difference perpendicular to the motion direction of the target object. New timestamps are attached to the raw TAE data to adjust temporary resolution to the same order of magnitude with the vertical axis in the TAE representation. After removing noisy and redundant events, designed templates are used to generate X-events to renovate broken lines and reproduce perpendicular edges. It is a real-time process which is unnecessary to wait for the collection of all the raw TAE data. A behavioral model of a 2 × 256 TAE vision sensor is established in Matlab, and X-events Generation block is realized in FPGA. Experimental results show that the proposed method can patch the TAE representation effectively to obtain a one-pixel-wide, precise, closed and connected contour.

Object Tracking Using Probabilistic Principal Component Analysis Based on Particle Filtering Framework

2011 ◽  
Vol 341-342 ◽  
pp. 790-797 ◽  
Author(s):  
Zhi Yan Xiang ◽  
Tie Yong Cao ◽  
Peng Zhang ◽  
Tao Zhu ◽  
Jing Feng Pan
Keyword(s):  
Principal Component Analysis ◽  
Object Tracking ◽  
Particle Filtering ◽  
Principal Component ◽  
Component Analysis ◽  
Dimensional Subspace ◽  
Target Object ◽  
Scale Invariant ◽  
Low Dimensional ◽  
Probabilistic Principal Component Analysis

In this paper, an object tracking approach is introduced for color video sequences. The approach presents the integration of color distributions and probabilistic principal component analysis (PPCA) into particle filtering framework. Color distributions are robust to partial occlusion, are rotation and scale invariant and are calculated efficiently. Principal Component Analysis (PCA) is used to update the eigenbasis and the mean, which can reflect the appearance changes of the tracked object. And a low dimensional subspace representation of PPCA efficiently adapts to these changes of appearance of the target object. At the same time, a forgetting factor is incorporated into the updating process, which can be used to economize on processing time and enhance the efficiency of object tracking. Computer simulation experiments demonstrate the effectiveness and the robustness of the proposed tracking algorithm when the target object undergoes pose and scale changes, defilade and complex background.

Bayesian framework for bilateral teleoperation systems over unreliable network

Robotica ◽  
2015 ◽  
Vol 34 (9) ◽  
pp. 2071-2086 ◽  
Author(s):  
Jae-young Lee ◽  
Shahram Payandeh
Keyword(s):  
Bayesian Approach ◽  
High Performance ◽  
Estimation Method ◽  
Bilateral Teleoperation ◽  
Real World Applications ◽  
Stochastic Framework ◽  
Efficient Prediction ◽  
True Position ◽  
Teleoperation Systems ◽  
Force Data

SUMMARYIn this paper, we present a novel stochastic framework for network-based bilateral teleoperation systems. A Bayesian approach, which provides robust tracking performance in real-world applications, is proposed to estimate and predict the stochastic variables and compensate for the unreliable network conditions. Combining with a practical approach in transport and application layers of the Internet, this paper demonstrates a high performance and efficient prediction and estimation method for bilateral teleoperation system. Experimental results show that the proposed Bayesian approach estimates and predicts true position and force data over unreliable network conditions, and therefore, improves the performance of overall teleoperation systems.

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