scholarly journals Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle

2019 ◽  
Vol 9 (16) ◽  
pp. 3359 ◽  
Author(s):  
Seokwon Yeom ◽  
In-Jun Cho
Keyword(s):  
Target Position ◽  
The State ◽  
Unmanned Aircraft ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
The Road ◽  
Detection And Tracking ◽  
Aerial Vehicle ◽  
Alarm Reduction ◽  
Average Position

Small unmanned aircraft vehicles (SUAVs) or drones are very useful for visual detection and tracking due to their efficiency in capturing scenes. This paper addresses the detection and tracking of moving pedestrians with an SUAV. The detection step consists of frame subtraction, followed by thresholding, morphological filter, and false alarm reduction, taking into consideration the true size of targets. The center of the detected area is input to the next tracking stage. Interacting multiple model (IMM) filtering estimates the state of vectors and covariance matrices, using multiple modes of Kalman filtering. In the experiments, a dozen people and one car are captured by a stationary drone above the road. The Kalman filter and the IMM filter with two or three modes are compared in the accuracy of the state estimation. The root-mean squared errors (RMSE) of position and velocity are obtained for each target and show the good accuracy in detecting and tracking the target position—the average detection rate is 96.5%. When the two-mode IMM filter is used, the minimum average position and velocity RMSE obtained are around 0.8 m and 0.59 m/s, respectively.

Spherical Simplex Unscented Kalman Filter-Based Jumping and Static Interacting Multiple Model

2017 ◽  
Vol 32 (04) ◽  
pp. 1850010
Author(s):  
Yi Pan ◽  
Hui Ye ◽  
Keke He
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filtering ◽  
System Parameter ◽  
Unscented Kalman Filter ◽  
Continuous System ◽  
The State ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Real Time System ◽  
Spherical Simplex

A modified interacting multiple model (IMM) method called spherical simplex unscented Kalman filter-based jumping and static IMM (SSUKF-JSIMM) is proposed to solve the problem of nonlinear filtering with unknown continuous system parameter. SSUKF-JSIMM regards the continuous system parameter space as a union of disjoint regions, and each region is assigned to a model. For each model, under the assumption that the parameter belongs to the corresponding region, one sub-filter is used to estimate the parameter and the state when the parameter is presumed to be jumping, and another sub-filter is used to estimate the parameter and the state when the parameter is presumed to be static. Considering that spherical simplex unscented Kalman filter (SSUKF) is more suitable for a real-time system than the unscented Kalman filter (UKF), SSUKFs are adopted as the sub-filters of SSUKF-JSIMM. Results of the two SSUKFs are fused as the estimation output of the model. Experimental results show that SSUKF-JSIMM achieves higher performance than IMM, SIR, and UKF in bearings-only tracking problem.

scholarly journals IMM Iterated Extended Particle Filter Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yang Wan ◽  
Shouyong Wang ◽  
Xing Qin
Keyword(s):  
Particle Filter ◽  
Density Function ◽  
The State ◽  
Arbitrary Distribution ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target ◽  
Particle Filter Algorithm ◽  
Non Gaussian ◽  
Importance Density Function

In order to solve the tracking problem of radar maneuvering target in nonlinear system model and non-Gaussian noise background, this paper puts forward one interacting multiple model (IMM) iterated extended particle filter algorithm (IMM-IEHPF). The algorithm makes use of multiple modes to model the target motion form to track any maneuvering target and each mode uses iterated extended particle filter (IEHPF) to deal with the state estimation problem of nonlinear non-Gaussian system. IEHPF is an improved particle filter algorithm, which utilizes iterated extended filter (IEHF) to obtain the mean value and covariance of each particle and describes importance density function as a combination of Gaussian distribution. Then according to the function, draw particles to approximate the state posteriori density of each mode. Due to the high filter accuracy of IEHF and the adaptation of system noise with arbitrary distribution as well as strong robustness, the importance density function generated by this method is more approximate to the true sate posteriori density. Finally, a numerical example is included to illustrate the effectiveness of the proposed methods.

scholarly journals Fault Detection and Isolation via the Interacting Multiple Model Approach Applied to Drive-By-Wire Vehicles

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2332 ◽  
Author(s):  
Vincent Judalet ◽  
Sébastien Glaser ◽  
Dominique Gruyer ◽  
Saïd Mammar
Keyword(s):  
Autonomous Vehicle ◽  
Fault Detection And Isolation ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Actuator Faults ◽  
Robust Identification ◽  
The Road ◽  
Driving Assistance ◽  
Experimental Vehicle ◽  
Model Approach

The place of driving assistance systems is currently increasing drastically for road vehicles. Paving the road to the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly for reliability reasons, thus requiring the development of diagnostic mechanisms. In this paper, we investigate the detection and the identification of sensor and actuator faults for a drive-by-wire road vehicle. An Interacting Multiple Model approach is proposed, based on a non-linear vehicle dynamics observer. The adequacy of different probabilistic observers is discussed. The results, based on experimental vehicle signals, show a fast and robust identification of sensor faults while the actuator faults are more challenging.

Tracking Filter for Radio Surveillance upon UAV Detection

2021 ◽  
Vol 26 (6) ◽  
pp. 554-564
Author(s):  
E.I. Minakov ◽  
◽  
G.A. Valikhin ◽  
A.V. Ovchinnikov ◽  
S.S. Matveeva ◽  
...  
Keyword(s):  
Surveillance System ◽  
High Efficiency ◽  
Target Position ◽  
Surveillance Systems ◽  
Energy Potential ◽  
Filter System ◽  
Detection And Tracking ◽  
Visual Identification ◽  
Aerial Vehicle ◽  
Tracking Filter

Unsanctioned intrusion of unmanned aerial vehicle (UAV) on the territory of the guarded object is primarily detected by specialized radio surveillance systems. The results obtained by radio surveillance systems are used for aiming of UAV visual identification and radio jamming systems. In this work, the problems of UAV detection and tracking of the target trajectory are considered. The known tracking filter systems for radio surveillance application were analyzed and a specialized matrix tracking filter system was proposed, which uses in its algorithm a dynamically changing energy potential of the radio surveillance system. The developed tracking filter system efficiency is evaluated using methods of matrix calculation, mathematical modeling, and probability theory. It has been established that the developed tracking filter system lets the radio surveillance equipment most effectively initiate trajectories of UAV, set its movement window, consider radio surveillance equipment characteristics, and approximate the trajectory of UAV at times of missed detections connected to radar cross-section fluctuations of moving targets. A high efficiency of the developed system has been achieved by decreasing the inaccuracy of the target position prediction two times in comparison with the known tracking filter systems. The obtained results allow easy scaling of the developed tracking filter system for its application as a part of any radio surveillance system.

scholarly journals Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 65
Author(s):  
Seokwon Yeom
Keyword(s):  
Thermal Imaging ◽  
Nearest Neighbor ◽  
Interacting Multiple Model ◽  
Morphological Operations ◽  
Multiple Model ◽  
Infrared Thermal Imaging ◽  
The Third ◽  
Detection And Tracking ◽  
Image Characteristics ◽  
Measured Position

Infrared (IR) thermal imaging can detect the warm temperature of the human body regardless of the light conditions, thus small drones equipped with the IR thermal camera can be utilized to recognize human activity for smart surveillance, road safety, and search and rescue missions. However, the unpredictable motion of the drone poses more challenges than a fixed camera. This paper addresses the detection and tracking of people through IR thermal video captured by a multirotor. For object detection, each frame is first registered with a reference frame to compensate for its coordinates. Then, the objects in each frame are segmented through k-means clustering and morphological operations. Falsely detected objects are removed considering the actual size and the shape of the object. The centroid of the segmented area is considered the measured position for target tracking. The track is initialized with two-point differencing initialization, and the target states are continuously estimated by the interacting multiple model (IMM) filter. The nearest neighbor association rule assigns the measurement to the track. Tracks that move slower than the minimum speed are terminated at the proposed criteria. In the experiments, three videos were captured with a long-wave IR band thermal imaging camera mounted on a multirotor. In the first and second videos, eight pedestrians on a pavement and three hikers on a mountain on winter nights were captured, respectively. In the third video, two walking people with complex backgrounds were captured on a windy summer day. The image characteristics vary between videos depending on the climate and surrounding objects, but the proposed scheme shows the robust performance in all cases; the average root mean squared errors in position and velocity are obtained as 0.08 m and 0.53 m/s, respectively for the first video, 0.06 m and 0.58 m/s, respectively for the second video, and 0.18 m and 1.84 m/s, respectively for the third video. The proposed method reduces false tracks from 10 to 1 in the third video.

scholarly journals Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators

2021 ◽  
Vol 11 (18) ◽  
pp. 8279
Author(s):  
Antun Ivanovic ◽  
Lovro Markovic ◽  
Marko Car ◽  
Ivan Duvnjak ◽  
Matko Orsag
Keyword(s):  
Visual Detection ◽  
Impedance Control ◽  
Contact Point ◽  
Target Position ◽  
Target Area ◽  
The Road ◽  
Aerial Manipulator ◽  
Bridge Inspections ◽  
Aerial Vehicle ◽  
Road Closure

Periodic bridge inspections are required every several years to determine the state of a bridge. Most commonly, the inspection is performed using specialized trucks allowing human inspectors to review the conditions underneath the bridge, which requires a road closure. The aim of this paper was to use aerial manipulators to mount sensors on the bridge to collect the necessary data, thus eliminating the need for the road closure. To do so, a two-step approach is proposed: an unmanned aerial vehicle (UAV) equipped with a pressurized canister sprays the first glue component onto the target area; afterward, the aerial manipulator detects the precise location of the sprayed area, and mounts the required sensor coated with the second glue component. The visual detection is based on an Red Green Blue - Depth (RGB-D) sensor and provides the target position and orientation. A trajectory is then planned based on the detected contact point, and it is executed through the adaptive impedance control capable of achieving and maintaining a desired force reference. Such an approach allows for the two glue components to form a solid bond. The described pipeline is validated in a simulation environment while the visual detection is tested in an experimental environment.

Unmanned aerial vehicle trajectory data fusion based on an active and passive feedback system

2019 ◽  
Vol 234 (3) ◽  
pp. 887-895
Author(s):  
Xin-min Tang ◽  
Ji-da Chen ◽  
Teng Li
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Convex Combination ◽  
Feedback System ◽  
Surveillance Data ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Trajectory Data ◽  
Fusion Algorithm ◽  
Time Operation ◽  
Aerial Vehicle

The surveillance data obtained from an unmanned aerial vehicle during real-time operation are very important for unmanned aerial vehicle supervision. In this study, in order to obtain more reliable and accurate unmanned aerial vehicle flight paths, we have proposed a trajectory fusion method of unmanned aerial vehicle data based on an active and passive feedback system. The active surveillance data are obtained from automatic dependent surveillance – broadcast and the passive surveillance data are derived from the unmanned aerial vehicle ground control station. The convex combination fusion algorithm is employed to fuse the two sets of data to obtain the accurate flight state and a continuous stable running track for the unmanned aerial vehicle. The simulation results showed that the fusion trajectory obtained using the convex combination fusion algorithm was smoother than the local trajectory obtained by the interacting multiple model algorithm.

scholarly journals Fuzzy-Logic-Based, Obstacle Information-Aided Multiple-Model Target Tracking

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 48 ◽  
Author(s):  
Quanhui Wang ◽  
En Fan ◽  
Pengfei Li
Keyword(s):  
Fuzzy Logic ◽  
Target Tracking ◽  
Transition Probability ◽  
Target Position ◽  
Transition Probability Matrix ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Convex Polygons ◽  
Maneuvering Target Tracking ◽  
Inference Systems

Incorporating obstacle information into maneuvering target-tracking algorithms may lead to a better performance when the target when the target maneuver is caused by avoiding collision with obstacles. In this paper, we propose a fuzzy-logic-based method incorporating new obstacle information into the interacting multiple-model (IMM) algorithm (FOIA-MM). We use convex polygons to describe the obstacles and then extract the distance from and the field angle of these obstacle convex polygons to the predicted target position as obstacle information. This information is fed to two fuzzy logic inference systems; one system outputs the model weights to their probabilities, the other yields the expected sojourn time of the models for the transition probability matrix assignment. Finally, simulation experiments and an Unmanned Aerial Vehicle experiment are carried out to demonstrate the efficiency and effectiveness of the proposed algorithm.

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