Vision based victim detection from unmanned aerial vehicles

Andriluka, Mykhaylo; Schnitzspan, Paul; Meyer, Johannes; Kohlbrecher, Stefan; Petersen, Kai; Stryk, Oskar von; Roth, Stefan; Schiele, Bernt

doi:10.1109/iros.2010.5649223

Cited by 94 publications

(64 citation statements)

References 29 publications

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“…Notably prior work does not address autonomy within this context [19,20] and work considering multi-modal detection is in its infancy [7,17,18]. Of those aerial techniques addressing autonomous target detection most borrow heavily from the state of the art in generalised object detection such as [7,17,21,22] but with often aerial detection specific enhancements (e.g. [7,22]).…”

Section: Introductionmentioning

confidence: 99%

Multi-modal target detection for autonomous wide area search and surveillance

et al. 2013

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Generalised wide are search and surveillance is a common-place tasking for multi-sensory equipped autonomous systems. Here we present on a key supporting topic to this task -the automatic interpretation, fusion and detected target reporting from multi-modal sensor information received from multiple autonomous platforms deployed for wide-area environment search. We detail the realization of a real-time methodology for the automated detection of people and vehicles using combined visible-band (EO), thermal-band (IR) and radar sensing from a deployed network of multiple autonomous platforms (ground and aerial). This facilities real-time target detection, reported with varying levels of confidence, using information from both multiple sensors and multiple sensor platforms to provide environment-wide situational awareness. A range of automatic classification approaches are proposed, driven by underlying machine learning techniques, that facilitate the automatic detection of either target type with cross-modal target confirmation. Extended results are presented that show both the detection of people and vehicles under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance evaluation is presented at an episodic level with individual classifiers optimized for maximal each object of interest (vehicle/person) detection over a given search path/pattern of the environment, across all sensors and modalities, rather than on a per sensor sample basis. Episodic target detection, evaluated over a number of wide-area environment search and reporting tasks, generally exceeds 90%+ for the targets considered here.

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Section: Introductionmentioning

confidence: 99%

Multi-modal target detection for autonomous wide area search and surveillance

et al. 2013

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“…This system functioned efficiently during disaster response and provided efficient data fusion from its sensor suite. Andriluka et al (2010) showed that vision-based human detection algorithm from UAV imagery produced feasible workflows combining various computer vision detectors such as pictorial structures (Andriluka et al, 2009) and discriminately trained models (Felzenszwalb et al, 2009), similar to components of Support Vector Machine (SVM). Rudol et al, (2008) introduced human body detection via positioning algorithms using visible and infrared imagery.…”

Section: State-of-the-art Geospatial Data Acquisition and Processing mentioning

confidence: 99%

Feasibility Study of Inexpensive Thermal Sensors and Small Uas Deployment for Living Human Detection in Rescue Missions Application Scenarios

Levin¹,

Zarnowski²,

McCarty³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Significant efforts are invested by rescue agencies worldwide to save human lives during natural and man-made emergency situations including those that happen in wilderness locations. These emergency situations include but not limited to: accidents with alpinists, mountainous skiers, people hiking and lost in remote areas. Sometimes in a rescue operation hundreds of first responders are involved to save a single human life. There are two critical issues where geospatial imaging can be a very useful asset in rescue operations support: 1) human detection and 2) confirming a fact that detected a human being is alive. International group of researchers from the Unites States and Poland collaborated on a pilot research project devoted to identify a feasibility of use for the human detection and alive-human state confirmation small unmanned aerial vehicles (SUAVs) and inexpensive forward looking infrared (FLIR) sensors. Equipment price for both research teams was below $8,000 including 3DR quadrotor UAV and Lepton longwave infrared (LWIR) imager which costs around $250 (for the US team); DJI Inspire 1 UAS with commercial Tamarisc-320 thermal camera (for the Polish team). Specifically both collaborating groups performed independent experiments in the USA and Poland and shared imaging data of on the ground and airborne electro-optical and FLIR sensor imaging collected. In these experiments dead bodies were emulated by use of medical training dummies. Real humans were placed nearby as live human subjects. Electro-optical imagery was used for the research in optimal human detection algorithms. Furthermore, given the fact that a dead human body after several hours has a temperature of the surrounding environment our experiments were challenged by the SUAS data optimization, i.e., distance from SUAV to object so that the FLIR sensor is still capable to distinguish temperature differences between a dummy and a real human. Our experiments indicated feasibility of use SUAVs and small thermal sensors for the human detection scenarios described above. Differences in temperatures were collected by deployed imaging acquisition platform are interpretable on FLIR images visually. Moreover, we applied ENVI image processing functions for calibration and numerical estimations of such a temperature differences. There are more potential system functionalities such as voice messages from rescue teams and even distant medication delivery for the victims of described emergencies. This paper describes experiments, processing results, and future research in more details.

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“…In a different way Andriluka et al (2010) evaluate various existing detection methods for detecting victims at nearby distances. According to this study, part-based detectors are better suited for victim detection from a UAV because they natively take into account the articulation of the human body.…”

Section: Existing Work For Detecting Human From the Airmentioning

confidence: 99%

How to improve the HOG detector in the UAV context

Blondel

Potelle

Pégard

et al. 2013

IFAC Proceedings Volumes

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The well known HOG (Histogram of Oriented Gradients) of Dalal and Triggs is commonly used for pedestrian detection from 2d moving embedded cameras (driving assistance) or static cameras (video surveillance). In this paper we show how to use and improve the HOG detector in the UAV context. In order to increase the elevation angular robustness we propose to use a more appropriate training dataset and sliding windows. We show results on synthetic images.

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Vision based victim detection from unmanned aerial vehicles

Cited by 94 publications

References 29 publications

Multi-modal target detection for autonomous wide area search and surveillance

Multi-modal target detection for autonomous wide area search and surveillance

Feasibility Study of Inexpensive Thermal Sensors and Small Uas Deployment for Living Human Detection in Rescue Missions Application Scenarios

How to improve the HOG detector in the UAV context

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