217,354 research outputs found
Using the discrete hadamard transform to detect moving objects in surveillance video
In this paper we present an approach to object detection in surveillance video based on detecting moving edges
using the Hadamard transform. The proposed method is characterized by robustness to illumination changes
and ghosting effects and provides high speed detection, making it particularly suitable for surveillance applications.
In addition to presenting an approach to moving edge detection using the Hadamard transform, we
introduce two measures to track edge history, Pixel Bit Mask Difference (PBMD) and History Update Value
(H UV ) that help reduce the false detections commonly experienced by approaches based on moving edges.
Experimental results show that the proposed algorithm overcomes the traditional drawbacks of frame differencing
and outperforms existing edge-based approaches in terms of both detection results and computational
complexity
3D Bounding Box Estimation Using Deep Learning and Geometry
We present a method for 3D object detection and pose estimation from a single
image. In contrast to current techniques that only regress the 3D orientation
of an object, our method first regresses relatively stable 3D object properties
using a deep convolutional neural network and then combines these estimates
with geometric constraints provided by a 2D object bounding box to produce a
complete 3D bounding box. The first network output estimates the 3D object
orientation using a novel hybrid discrete-continuous loss, which significantly
outperforms the L2 loss. The second output regresses the 3D object dimensions,
which have relatively little variance compared to alternatives and can often be
predicted for many object types. These estimates, combined with the geometric
constraints on translation imposed by the 2D bounding box, enable us to recover
a stable and accurate 3D object pose. We evaluate our method on the challenging
KITTI object detection benchmark both on the official metric of 3D orientation
estimation and also on the accuracy of the obtained 3D bounding boxes. Although
conceptually simple, our method outperforms more complex and computationally
expensive approaches that leverage semantic segmentation, instance level
segmentation and flat ground priors and sub-category detection. Our
discrete-continuous loss also produces state of the art results for 3D
viewpoint estimation on the Pascal 3D+ dataset.Comment: To appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
Automated Visual Fin Identification of Individual Great White Sharks
This paper discusses the automated visual identification of individual great
white sharks from dorsal fin imagery. We propose a computer vision photo ID
system and report recognition results over a database of thousands of
unconstrained fin images. To the best of our knowledge this line of work
establishes the first fully automated contour-based visual ID system in the
field of animal biometrics. The approach put forward appreciates shark fins as
textureless, flexible and partially occluded objects with an individually
characteristic shape. In order to recover animal identities from an image we
first introduce an open contour stroke model, which extends multi-scale region
segmentation to achieve robust fin detection. Secondly, we show that
combinatorial, scale-space selective fingerprinting can successfully encode fin
individuality. We then measure the species-specific distribution of visual
individuality along the fin contour via an embedding into a global `fin space'.
Exploiting this domain, we finally propose a non-linear model for individual
animal recognition and combine all approaches into a fine-grained
multi-instance framework. We provide a system evaluation, compare results to
prior work, and report performance and properties in detail.Comment: 17 pages, 16 figures. To be published in IJCV. Article replaced to
update first author contact details and to correct a Figure reference on page
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