An Enhanced Viola-Jones Vehicle Detection Method From Unmanned Aerial Vehicles Imagery

Xu, Yongzheng; Yu, Guizhen; Wu, Xinkai; Wang, Yunpeng; Ma, Yalong

doi:10.1109/tits.2016.2617202

Cited by 91 publications

(57 citation statements)

References 30 publications

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“…The most popular platform so far is OpenCV (Aguilar, 2017;Rudol & Doherty, 2018;Xu, Yu, Wu, Wang, & Ma, 2017). Accuracy is also specified in most the cases although some of the algorithms are evaluated in their own data set.…”

Section: Uav Analysismentioning

confidence: 99%

Smartphone‐based object recognition with embedded machine learning intelligence for unmanned aerial vehicles

Martinez‐Alpiste

Casaseca‐de‐la‐Higuera

Alcaraz-Calero

et al. 2019

Journal of Field Robotics

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Existing artificial intelligence solutions typically operate in powerful platforms with high computational resources availability. However, a growing number of emerging use cases such as those based on unmanned aerial systems (UAS) require new solutions with embedded artificial intelligence on a highly mobile platform. This paper proposes an innovative UAS that explores machine learning (ML) capabilities in a smartphone‐based mobile platform for object detection and recognition applications. A new system framework tailored to this challenging use case is designed with a customized workflow specified. Furthermore, the design of the embedded ML leverages TensorFlow, a cutting‐edge open‐source ML framework. The prototype of the system integrates all the architectural components in a fully functional system, and it is suitable for real‐world operational environments such as seek and rescue use cases. Experimental results validate the design and prototyping of the system and demonstrate an overall improved performance compared with the state of the art in terms of a wide range of metrics.

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Section: Uav Analysismentioning

confidence: 99%

Smartphone‐based object recognition with embedded machine learning intelligence for unmanned aerial vehicles

Martinez‐Alpiste

Casaseca‐de‐la‐Higuera

Alcaraz-Calero

et al. 2019

Journal of Field Robotics

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“…Second, object tracking algorithms are proposed for trajectory reconstruction. Region-based tracking algorithms [8], feature-based tracking algorithms [25], and model-based tracking algorithms [19] have all been widely applied for various outputs.…”

Section: Related Workmentioning

confidence: 99%

Local Fast R-CNN Flow for Object-Centric Event Recognition in Complex Traffic Scenes

Yang

Yan

et al. 2018

Image and Video Technology

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This paper presents a solution for an integrated object-centric event recognition problem for intelligent traffic supervision. We propose a novel event-recognition framework using deep local flow in a fast regionbased convolutional neural network (R-CNN). First, we use a fine-tuned fast R-CNN to accurately extract multi-scale targets in the open environment. Each detected object corresponds to an event candidate. Second, a deep belief propagation method is proposed for the calculation of local fast R-CNN flow (LFRCF) between local convolutional feature matrices of two non-adjacent frames in a sequence. Third, by using the LFRCF features, we can easily identify the moving pattern of each extracted object and formulate a conclusive description of each event candidate. The contribution of this paper is to propose an optimized framework for accurate event recognition. We verify the accuracy of multi-scale object detection and behavior recognition in extensive experiments on real complex road-intersection surveillance videos.

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“…The Viola & Jones algorithm [34] has been used to recognize the car along the path. Although the algorithm was originally designed and developed for face detection problem, it can be trained to detect any object [35] by using different features types (Haar, HOG, LBP) [27] and training stages.…”

Section: Classifier Synthesismentioning

confidence: 99%

An educational simulation platform for GPS-denied unmanned Aerial Vehicles aimed to the detection and tracking of moving objects

Silano

Iannelli

2016

2016 IEEE Conference on Control Applications (CCA)

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The main motivation of this work is to propose a simulation approach for a specific task within the UAV (Unmanned Aerial Vehicle) field, i.e., the visual detection and tracking of arbitrary moving objects. In particular, it is described MAT-Fly, a numerical simulation platform for multi-rotors aircraft characterized by the ease of use and control development. The platform is based on Matlab R and the MathWorks TM Virtual Reality (VR) and Computer Vision System (CVS) toolboxes that work together to simulate the behavior of a drone in a 3D environment while tracking a car that moves along a non trivial path.The VR toolbox has been chosen due to the familiarity that students have with Matlab and because it allows to move the attention to the classifier, the tracker, the reference generator and the trajectory tracking control thanks to its simple structure. The overall architecture is quite modular so that each block can be easily replaced with others by simplifying the development phase and by allowing to add even more functionalities.The simulation platform makes easy and quick to insert and to remove flight control system components, testing and comparing different plans when computer vision algorithms are in the loop. In an automatic way, the proposed simulator is able to acquire frames from the virtual scenario, to search for one or more objects on which it has been trained during the learning phase, and to track the target position applying a trajectory control addressing what is well-known in the literature as an image-based visual servoing problem. Some simple testbeds have been presented in order to show the effectiveness and robustness of the proposed approach as well as the platform works. We released the software as open-source, making it available for educational purposes.Keywords image-based visual servoing · 3D simulation scenario · MATLAB/Simulink · multi-rotors · educational · vision detection and tracking · UAV · software-in-the-loop · computer vision · trajectory control

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An Enhanced Viola-Jones Vehicle Detection Method From Unmanned Aerial Vehicles Imagery

Cited by 91 publications

References 30 publications

Smartphone‐based object recognition with embedded machine learning intelligence for unmanned aerial vehicles

Smartphone‐based object recognition with embedded machine learning intelligence for unmanned aerial vehicles

Local Fast R-CNN Flow for Object-Centric Event Recognition in Complex Traffic Scenes

An educational simulation platform for GPS-denied unmanned Aerial Vehicles aimed to the detection and tracking of moving objects

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