Deep Convolutional Neural Network For Human Detection And Tracking In FLIR Videos

Sathyan, Anoop; Cohen, Joseph; Kumar, Manish

doi:10.2514/6.2016-1412

Cited by 1 publication

(1 citation statement)

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“…With the rise of deep learning, the application of CNNs started to become well-established for the task of human detection from aerial views [4,15,25,55]. By comparing against more classical feature extractors and detectors such as Haar, HOG or SVM, an improvement in detection performance as well as a better generalization when using CNNs, was stated throughout.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-based Human Detection for UAVs with Optical and Infrared Cameras: System and Experiments

Hinzmann,

Stegemann,

Cadena

et al. 2020

Preprint

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In this paper, we present our deep learning-based human detection system that uses optical (RGB) and long-wave infrared (LWIR) cameras to detect, track, localize, and re-identify humans from UAVs flying at high altitude. In each spectrum, a customized RetinaNet network with ResNet backbone provides human detections which are subsequently fused to minimize the overall false detection rate. We show that by optimizing the bounding box anchors and augmenting the image resolution the number of missed detections from high altitudes can be decreased by over 20 percent. Our proposed network is compared to different RetinaNet and YOLO variants, and to a classical optical-infrared human detection framework that uses hand-crafted features. Furthermore, along with the publication of this paper, we release a collection of annotated optical-infrared datasets recorded with different UAVs during search-and-rescue field tests and the source code of the implemented annotation tool.

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Section: Related Workmentioning

confidence: 99%