952 research outputs found
Fast Single Shot Detection and Pose Estimation
For applications in navigation and robotics, estimating the 3D pose of
objects is as important as detection. Many approaches to pose estimation rely
on detecting or tracking parts or keypoints [11, 21]. In this paper we build on
a recent state-of-the-art convolutional network for slidingwindow detection
[10] to provide detection and rough pose estimation in a single shot, without
intermediate stages of detecting parts or initial bounding boxes. While not the
first system to treat pose estimation as a categorization problem, this is the
first attempt to combine detection and pose estimation at the same level using
a deep learning approach. The key to the architecture is a deep convolutional
network where scores for the presence of an object category, the offset for its
location, and the approximate pose are all estimated on a regular grid of
locations in the image. The resulting system is as accurate as recent work on
pose estimation (42.4% 8 View mAVP on Pascal 3D+ [21] ) and significantly
faster (46 frames per second (FPS) on a TITAN X GPU). This approach to
detection and rough pose estimation is fast and accurate enough to be widely
applied as a pre-processing step for tasks including high-accuracy pose
estimation, object tracking and localization, and vSLAM
Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery
Can we improve detection in the thermal domain by borrowing features from
rich domains like visual RGB? In this paper, we propose a pseudo-multimodal
object detector trained on natural image domain data to help improve the
performance of object detection in thermal images. We assume access to a
large-scale dataset in the visual RGB domain and relatively smaller dataset (in
terms of instances) in the thermal domain, as is common today. We propose the
use of well-known image-to-image translation frameworks to generate pseudo-RGB
equivalents of a given thermal image and then use a multi-modal architecture
for object detection in the thermal image. We show that our framework
outperforms existing benchmarks without the explicit need for paired training
examples from the two domains. We also show that our framework has the ability
to learn with less data from thermal domain when using our approach. Our code
and pre-trained models are made available at
https://github.com/tdchaitanya/MMTODComment: Accepted at Perception Beyond Visible Spectrum Workshop, CVPR 201
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