Convolutional Neural Network-Based Real-Time Object Detection and Tracking for Parrot AR Drone 2

Rohan, Ali; Rabah, Mohammed; Kim, Sung Hoon

doi:10.1109/access.2019.2919332

Cited by 99 publications

(37 citation statements)

References 18 publications

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“…This section covers the following; 1) a vision-based algorithm is used to calculate the absolute position of the quadcopter in real-time, 2) the proposed algorithm is compared to a developed PID controller that is used for Human-follow [25], and how much each algorithm takes to be executed, 3) the proposed algorithm is compared to the previous mentioned one to track the RC car in a rectangular path under variable speed (2 m/s and 4 m/s), and the results of their performance are carried out.…”

Section: ) Object Tracking Resultsmentioning

confidence: 99%

Heterogeneous Parallelization for Object Detection and Tracking in UAVs

et al. 2020

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Recent technical advancements in both fields of unmanned aerial vehicles (UAV) control and artificial intelligence (AI) have made a certain realm of applications possible. However, one of the main problems in integration of these two areas is the bottleneck of computing AI applications on UAV's resource limited platform. One of the main solution for this problem is that AI and control software from one side and computing hardware mounted on UAV from the other side be adopted together based on the main constraints of the resource limited computing platform on UAV. Basically, the target constraints of such adaptation are performance, energy efficiency, and accuracy. In this paper, we propose a strategy to integrate and adopt the commonly used object detection and tracking algorithm and UAV control software to be executed on a heterogeneous resource limited computing units on a UAV. For object detection, a convolutional neural network (CNN) algorithm is used. For object tracking, a novel algorithm is proposed that can execute along with object tracking via sequential stream data. For UAV control, a Gain-Scheduled PID controller is designed that steers the UAV by continuously manipulation of the actuators based on the stream data from the tracking unit and dynamics of the UAV. All the algorithms are adopted to be executed on a heterogeneous platform including NVIDIA Jetson TX2 embedded computer and an ARM Cortex M4. The observation from real-time operation of the platform shows that using the proposed platform reduces the power consumption by 53.69% in contrast with other existing methods while having marginal penalty for object detection and tracking parts.

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Section: ) Object Tracking Resultsmentioning

confidence: 99%

Heterogeneous Parallelization for Object Detection and Tracking in UAVs

et al. 2020

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“…( In order to compute the overall performance of the classifier, we define a composite score metric [30]. The reason to create such matric is to measure overall performance including the FPS which is a very important parameter to analyze computer vision and image processing applications.…”

Section: Resultsmentioning

confidence: 99%

Human Pose Estimation-Based Real-Time Gait Analysis Using Convolutional Neural Network

Rohan

Rabah

Hosny³

et al. 2020

IEEE Access

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Gait analysis is widely used in clinical practice to help in understanding the gait abnormalities and its association with a certain underlying medical condition for better diagnosis and prognosis. Several technologies embedded in the specialized devices such as computer-interfaced video cameras to measure patient motion, electrodes placed on the surface of the skin to appreciate muscle activity, force platforms embedded in a walkway to monitor the forces and torques produced between the ambulatory patient and the ground, Inertial Measurement Unit (IMU) sensors, and wearable devices are being used for this purpose. All of these technologies require an expert to translate the data recorded by the said embedded specialized devices, which is typically done by a medical expert but with the recent improvements in the field of Artificial Intelligence (AI), especially in deep learning, it is possible now to create a mechanism where the translation of the data can be performed by a deep learning tool such as Convolutional Neural Network (CNN). Therefore, this work presents an approach where human pose estimation is combined with a CNN for classification between normal and abnormal gait of a human with an ability to provide information about the detected abnormalities form an extracted skeletal image in real-time.

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“…The world of computer vision has been reborn since the introduction of convolutional neural networks by [24]. Methods based on CNNs have achieved state-of-the-art performance in many tasks, such as image classification [25]- [30], object detection [31]- [35], and semantic segmentation [36], [37]. In recent years, the field of medical image analysis has attracted the attention of many researchers [38]- [42].…”

Section: Related Workmentioning

confidence: 99%

PartMitosis: A Partially Supervised Deep Learning Framework for Mitosis Detection in Breast Cancer Histopathology Images

2020

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Detection of mitotic tumor cells per tissue area is one of the critical markers of breast cancer prognosis. The aim of this paper is to develop a method for the automatic detection of mitotic figures from breast cancer histological slides using a partially supervised deep learning framework. Unlike the previous literature, which has focused on solving the problem of mitosis detection in the weakly annotated datasets using centroid pixel labels (weak labels) only without taking advantage of the available pixel-level labels (strong labels) of other datasets, in this paper, we design a novel partially supervised framework based on two parallel deep fully convolutional networks. One of them is trained using weak labels and the other is trained using strong labels, together with a weight transfer function. In the detection phase, we fuse the segmentation maps produced by the two networks to obtain the final mitosis detections. Our system exploits the available large sets of mitosis detection samples with mitosis centroid annotation, such as the 2014 ICPR dataset and the AMIDA13 dataset, and only a small set of samples with the annotation of all mitosis pixels, such as the 2012 ICPR dataset, to perform a more accurate mitosis detection on weakly labeled data. This enables us to outperform all previous mitosis detection systems by achieving F-scores of 0.575 and 0.698 on the 2014 ICPR dataset and the AMIDA13 dataset respectively.

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Convolutional Neural Network-Based Real-Time Object Detection and Tracking for Parrot AR Drone 2

Cited by 99 publications

References 18 publications

Heterogeneous Parallelization for Object Detection and Tracking in UAVs

Heterogeneous Parallelization for Object Detection and Tracking in UAVs

Human Pose Estimation-Based Real-Time Gait Analysis Using Convolutional Neural Network

PartMitosis: A Partially Supervised Deep Learning Framework for Mitosis Detection in Breast Cancer Histopathology Images

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