A video-based object detection and tracking system for weight sensitive UAVs

Abughalieh, Karam; Sababha, Belal H.; Rawashdeh, Nathir A.

doi:10.1007/s11042-018-6508-1

Cited by 27 publications

(23 citation statements)

References 26 publications

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“…The majority of conventional methods for visual motion discrimination focus on distinguishing between the physical difference in the foregrounds and backgrounds, including their size, shape and texture details [26]- [28]. So far, preliminary visual motion perception techniques utilise three typical methods: geometric feature detection [29]- [31], background subtraction [32], [33] and optical flow [34], [35].…”

Section: Related Work a Conventional Methods Of Visual Motion DImentioning

confidence: 99%

Coping With Multiple Visual Motion Cues Under Extremely Constrained Computation Power of Micro Autonomous Robots

Chen

Peng

et al. 2020

IEEE Access

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The perception of different visual motion cues is crucial for autonomous mobile robots to react to or interact with the dynamic visual world. It is still a great challenge for a micro mobile robot to cope with dynamic environments due to the restricted computational resources and the limited functionalities of its visual systems. In this study, we propose a compound visual neural system to automatically extract and fuse different visual motion cues in real-time using the extremely constrained computation power of micro mobile robots. The proposed visual system contains multiple bio-inspired visual motion perceptive neurons each with a unique role, for example to extract collision visual cues, darker collision cue and directional motion cues. In the embedded system, these multiple visual neurons share a similar presynaptic network to minimise the consumption of computation resources. In the postsynaptic part of the system, visual cues pass results to corresponding action neurons using lateral inhibition mechanism. The translational motion cues, which are identified by comparing pairs of directional cues, are given the highest priority, followed by the darker colliding cues and approaching cues. Systematic experiments with both virtual visual stimuli and real-world scenarios have been carried out to validate the system's functionality and reliability. The proposed methods have demonstrated that (1) with extremely limited computation power, it is still possible for a micro mobile robot to extract multiple visual motion cues robustly in a complex dynamic environment; (2) the cues extracted can be fused with a lateral inhibited postsynaptic network, thus enabling the micro robots to respond effectively with different actions, accordingly to different states, in real-time. The proposed embedded visual system has been modularised and can be easily implemented in other autonomous mobile platforms for real-time applications. The system could also be used by neurophysiologists to test new hypotheses pertaining to biological visual neural systems. INDEX TERMS Bio-inspired, Neural Network, Visual Motion Perception, Micro Robot, Multiple Cues. I. INTRODUCTION C OMPUTER vision has underpinned the rapid development of autonomous mobile robots in various applications, such as surveillance, transportation and manipulation [1]-[3]. A distinctive feature of computer vision is that its performance is strictly determined by the scale of available computational resources [4], [5]. In many remote robotics applications, such as ruin investigation [6], moon rovers [7] and underwater surveillance [8], micro mobile

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Section: Related Work a Conventional Methods Of Visual Motion DImentioning

confidence: 99%

Coping With Multiple Visual Motion Cues Under Extremely Constrained Computation Power of Micro Autonomous Robots

Chen

Peng

et al. 2020

IEEE Access

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“…However, at (t ¼ 4.5) and object out of range message is logged to denote that the UAV has lost sight of the tall building. It can also be seen that there are messages, denoted in blue, sent by UAV 1 at (t 5,10,15,20). These are the periodic messages a UAV periodically broadcasts based on a preconfigured time interval.…”

Section: First Scenario: Basic Operationmentioning

confidence: 99%

“…8 Another field of research is the integration of computer vision algorithms, visual localization and mapping, obstacle detection, and target tracking with UAVs. [9][10][11][12] Although governmental entities' efforts are still undergoing to develop governance and rules to fly small UAVs for various commercial and other purposes, some of the main guidelines and rules started appearing recently. The Federal Aviation Administration (FAA) released in the second half of 2016 the rules (Part 107) for operating small UAVs weighing below 55 lbs (25 kg).…”

Section: Introductionmentioning

confidence: 99%

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UTSim: A framework and simulator for UAV air traffic integration, control, and communication

Al-Mousa

Sababha

Al-Madi

et al. 2019

International Journal of Advanced Robotic Systems

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The interest in unmanned systems especially unmanned aerial vehicle is continuously increasing. Unmanned aerial vehicles started to become of great benefit in many different fields. It is anticipated that unmanned aerial vehicles will soon become a main component of the future urban air traffic. The integration of unmanned aerial vehicles within existing air traffic environments has started getting the attention of researchers. Integrating unmanned systems in the real-world urban air traffic requires the development of tools and simulators to enable researchers in their ongoing efforts. In this article, a simulator called UTSim is introduced. The proposed simulator is built using the Unity platform. UTSim is capable of simulating unmanned aerial vehicle physical specification, navigation, control, communication, sensing and avoidance in environments with static and moving objects. The simulator enables studying and exploring several unmanned aerial vehicle air traffic integration issues like sense and avoid, communication protocols, navigation algorithms, and much more. UTSim is designed and developed to be easily used. The user can specify the properties of the environment, the number and types of unmanned aerial vehicles in the environment, and specify the algorithm to be used for path planning and collision avoidance. The simulator outputs a log file with a lot of useful information such as the number of sent and received messages, the number of detected objects and collided unmanned aerial vehicles. Three scenarios have been implemented in this article to present the capabilities of UTSim and to illustrate how it can benefit researchers in the field of integrating unmanned aerial vehicles in urban air traffic.

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“…Karam et al suggested an object detection and tracking system using SURF (Speededup Robust Features), SIFT (Scale-invariant Feature Transform), and FAST (Features from Accelerated Segment Test) algorithms to monitor cars from different altitudes and angles through a down-pointing camera [21]. Detecting small targets from a first-person view (FPV) camera is challenging.…”

Section: Introductionmentioning

confidence: 99%

Multiple Drone Navigation and Formation Using Selective Target Tracking-Based Computer Vision

2021

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Autonomous unmanned aerial vehicles work seamlessly within the GPS signal range, but their performance deteriorates in GPS-denied regions. This paper presents a unique collaborative computer vision-based approach for target tracking as per the image’s specific location of interest. The proposed method tracks any object without considering its properties like shape, color, size, or pattern. It is required to keep the target visible and line of sight during the tracking. The method gives freedom of selection to a user to track any target from the image and form a formation around it. We calculate the parameters like distance and angle from the image center to the object for the individual drones. Among all the drones, the one with a significant GPS signal strength or nearer to the target is chosen as the master drone to calculate the relative angle and distance between an object and other drones considering approximate Geo-location. Compared to actual measurements, the results of tests done on a quadrotor UAV frame achieve 99% location accuracy in a robust environment inside the exact GPS longitude and latitude block as GPS-only navigation methods. The individual drones communicate to the ground station through a telemetry link. The master drone calculates the parameters using data collected at ground stations. Various formation flying methods help escort other drones to meet the desired objective with a single high-resolution first-person view (FPV) camera. The proposed method is tested for Airborne Object Target Tracking (AOT) aerial vehicle model and achieves higher tracking accuracy.

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A video-based object detection and tracking system for weight sensitive UAVs

Cited by 27 publications

References 26 publications

Coping With Multiple Visual Motion Cues Under Extremely Constrained Computation Power of Micro Autonomous Robots

Coping With Multiple Visual Motion Cues Under Extremely Constrained Computation Power of Micro Autonomous Robots

UTSim: A framework and simulator for UAV air traffic integration, control, and communication

Multiple Drone Navigation and Formation Using Selective Target Tracking-Based Computer Vision

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