An intruder detection algorithm for vision based sense and avoid system

Zhang, Zhouyu; Cao, Yunfeng; Ding, Meng; Zhuang, Likui; Yao, Weiwen

doi:10.1109/icuas.2016.7502521

Cited by 27 publications

(15 citation statements)

References 8 publications

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“…The vision-based methods are almost the lowest cost and the most easily configured one. [1][2][3][4][5][6][7] Zhang et al 8 proposed an intruder detection algorithm based on learning deep features using video images. The sliding window technique is used to obtain the test samples.…”

Section: Vision-based Methodsmentioning

confidence: 99%

Acoustical unmanned aerial vehicle detection in indoor scenarios using logistic regression model

Iannace¹,

Ciaburro²,

Trematerra³

2020

Building Acoustics

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In this study, the data obtained from the acoustic measurements were used to train a model based on logistic regression in order to detect a quadrotor’s vehicle in indoor environment. To simulate a real environment, we made sound recordings in a shopping center. The sounds related to two scenarios were recorded: only anthropic noise and anthropic noise with background music. Later, we reproduced these sounds in an indoor environment of the same size and characteristics as the shopping center. During the simulation test, a drone placed at different distances from the sound level meter was turned on at different speeds to identify their presence in complex acoustic scenarios. Subsequently, these measurements were used to implement a model based on logistic regression for the automatic detection of the unmanned aerial vehicle. Logistic regression is widely used in pattern recognition of the binary dependent variable. This model returns high value of accuracy (0.994), indicating a high number of correct detections. The results obtained in this study suggest the use of this tool for unmanned aerial vehicle detection applications.

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Section: Vision-based Methodsmentioning

confidence: 99%

Acoustical unmanned aerial vehicle detection in indoor scenarios using logistic regression model

Iannace¹,

Ciaburro²,

Trematerra³

2020

Building Acoustics

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“…In the C-RPAS structure, there are electrooptical (EO) and infrared systems for video detection and RPAS recognition. A drone can be detected based on specific features such as color, contour lines, geometric shapes [6] or edges and other movement characteristics [7]. There are drones that simulate the flight of birds (Ornithoptera-According to DEX (explanatory dictionary), Ornithopter -Aircraft heavier than air, with flying wings, which mimics the flight of birds).…”

Section: The Influence Of Fingerprint Mitigation Devices On Visible and Infrared Spectrum From Rpas Structure On Detectionmentioning

confidence: 99%

The Existing Technologies on Anti-Drone Systems

Popescu

2021

International Conference KNOWLEDGE-BASED ORGANIZATION

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The paper presents the technologies currently available on the market in the field of anti-drone systems (C-RPAS -Counter Remotely Piloted Aircraft System). These include technologies with the help of radar, audio interception systems or via infrared and electro-optical devices, which are limited in remote sensing. The purpose of this paper was to highlight the multitude of factors that can influence the main mission of C-RPAS systems, the detection. Without detection the other features of a C-RPAS system could not be applied. I used specialized documents and studies, but also comparative analysis as research methods. The results of the study confirmed to me the hypothesis that anti-drone systems use in combination, one or more of the technologies (to detect, to recognize, to identify, to locate, to block, to capture or to destroy the drone). The first four (the detection, the recognition, the identification, the localization) are in the basic configuration for any C-RPAS system. In the future, there will be a challenge (for the producers of C-RPAS systems), the capture of the RPAS, especially the military ones. It is also important to prepare the operators / beneficiaries for such systems. They can influence the effectiveness of drone combat missions.

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“…Node term is measured by (4), where edge term is measured by (5). Where ω = [ω 1 , ω 2 ] is the CRF weights, and energy function E(C, Y , ω) can therefore be formulated as (6).…”

Section: ) the Layered Structure For Extracting Spatial Contextmentioning

confidence: 99%

Vision-Based Flying Targets Detection via Spatiotemporal Context Fusion

et al. 2019

Self Cite

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Deriving from the imperative necessities for developing Sense and Avoid (SAA) capability of Unmanned Aerial Vehicle (UAV), a newly designed flying targets detection algorithm is presented in this paper for enhancing the UAV environment perception ability. Since spatiotemporal context is crucial for insuring the effectiveness of flying targets detection, the algorithm is constructed on the basis of spatiotemporal context fusion. The algorithm proposed in this paper contains three parts, namely the spatial context extraction, temporal context extraction and spatiotemporal context fusion. 1) In order to extract spatial context, dense sampling method is firstly applied to obtain dense image grids, then spatial context is generated via pre-learned conditional random field (CRF) model using a layered structure: dense image patches, bottom feature descriptors, sparse codes, and predicted CRF labels. 2) In order to extract temporal context, the forward and back motion history image (FBMHI) is firstly computed for detecting motion cues, and the adaptive foreground and background isolation is further adopted for acquiring the temporal probability map. 3) The presence probability map of flying targets is finally obtained by spatiotemporal context fusion, and flying targets is therefore picked out by analyzing fused presence probability map. A set of videos containing different drone models are selected for evaluation, and the comparisons against other algorithms demonstrate superiority of the proposed algorithm.INDEX TERMS Sense and avoid, spatiotemporal, context fusion, conditional random field, sparse representation, forward and back motion history image.

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An intruder detection algorithm for vision based sense and avoid system

Cited by 27 publications

References 8 publications

Acoustical unmanned aerial vehicle detection in indoor scenarios using logistic regression model

Acoustical unmanned aerial vehicle detection in indoor scenarios using logistic regression model

The Existing Technologies on Anti-Drone Systems

Vision-Based Flying Targets Detection via Spatiotemporal Context Fusion

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