13,616 research outputs found
Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network
Drone systems have been deployed by various law enforcement agencies to
monitor hostiles, spy on foreign drug cartels, conduct border control
operations, etc. This paper introduces a real-time drone surveillance system to
identify violent individuals in public areas. The system first uses the Feature
Pyramid Network to detect humans from aerial images. The image region with the
human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network
for human pose estimation. The orientations between the limbs of the estimated
pose are next used to identify the violent individuals. The proposed deep
network can learn meaningful representations quickly using ScatterNet and
structural priors with relatively fewer labeled examples. The system detects
the violent individuals in real-time by processing the drone images in the
cloud. This research also introduces the aerial violent individual dataset used
for training the deep network which hopefully may encourage researchers
interested in using deep learning for aerial surveillance. The pose estimation
and violent individuals identification performance is compared with the
state-of-the-art techniques.Comment: To Appear in the Efficient Deep Learning for Computer Vision (ECV)
workshop at IEEE Computer Vision and Pattern Recognition (CVPR) 2018. Youtube
demo at this: https://www.youtube.com/watch?v=zYypJPJipY
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration
In this paper, we propose the 3DFeat-Net which learns both 3D feature
detector and descriptor for point cloud matching using weak supervision. Unlike
many existing works, we do not require manual annotation of matching point
clusters. Instead, we leverage on alignment and attention mechanisms to learn
feature correspondences from GPS/INS tagged 3D point clouds without explicitly
specifying them. We create training and benchmark outdoor Lidar datasets, and
experiments show that 3DFeat-Net obtains state-of-the-art performance on these
gravity-aligned datasets.Comment: 17 pages, 6 figures. Accepted in ECCV 201
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