5,459 research outputs found
PIXOR: Real-time 3D Object Detection from Point Clouds
We address the problem of real-time 3D object detection from point clouds in
the context of autonomous driving. Computation speed is critical as detection
is a necessary component for safety. Existing approaches are, however,
expensive in computation due to high dimensionality of point clouds. We utilize
the 3D data more efficiently by representing the scene from the Bird's Eye View
(BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs
oriented 3D object estimates decoded from pixel-wise neural network
predictions. The input representation, network architecture, and model
optimization are especially designed to balance high accuracy and real-time
efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection
benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets
we show that the proposed detector surpasses other state-of-the-art methods
notably in terms of Average Precision (AP), while still runs at >28 FPS.Comment: Update of CVPR2018 paper: correct timing, fix typos, add
acknowledgemen
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
The primate visual system achieves remarkable visual object recognition
performance even in brief presentations and under changes to object exemplar,
geometric transformations, and background variation (a.k.a. core visual object
recognition). This remarkable performance is mediated by the representation
formed in inferior temporal (IT) cortex. In parallel, recent advances in
machine learning have led to ever higher performing models of object
recognition using artificial deep neural networks (DNNs). It remains unclear,
however, whether the representational performance of DNNs rivals that of the
brain. To accurately produce such a comparison, a major difficulty has been a
unifying metric that accounts for experimental limitations such as the amount
of noise, the number of neural recording sites, and the number trials, and
computational limitations such as the complexity of the decoding classifier and
the number of classifier training examples. In this work we perform a direct
comparison that corrects for these experimental limitations and computational
considerations. As part of our methodology, we propose an extension of "kernel
analysis" that measures the generalization accuracy as a function of
representational complexity. Our evaluations show that, unlike previous
bio-inspired models, the latest DNNs rival the representational performance of
IT cortex on this visual object recognition task. Furthermore, we show that
models that perform well on measures of representational performance also
perform well on measures of representational similarity to IT and on measures
of predicting individual IT multi-unit responses. Whether these DNNs rely on
computational mechanisms similar to the primate visual system is yet to be
determined, but, unlike all previous bio-inspired models, that possibility
cannot be ruled out merely on representational performance grounds.Comment: 35 pages, 12 figures, extends and expands upon arXiv:1301.353
Fast object detection in compressed JPEG Images
Object detection in still images has drawn a lot of attention over past few
years, and with the advent of Deep Learning impressive performances have been
achieved with numerous industrial applications. Most of these deep learning
models rely on RGB images to localize and identify objects in the image.
However in some application scenarii, images are compressed either for storage
savings or fast transmission. Therefore a time consuming image decompression
step is compulsory in order to apply the aforementioned deep models. To
alleviate this drawback, we propose a fast deep architecture for object
detection in JPEG images, one of the most widespread compression format. We
train a neural network to detect objects based on the blockwise DCT (discrete
cosine transform) coefficients {issued from} the JPEG compression algorithm. We
modify the well-known Single Shot multibox Detector (SSD) by replacing its
first layers with one convolutional layer dedicated to process the DCT inputs.
Experimental evaluations on PASCAL VOC and industrial dataset comprising images
of road traffic surveillance show that the model is about faster than
regular SSD with promising detection performances. To the best of our
knowledge, this paper is the first to address detection in compressed JPEG
images
Sequence to Sequence -- Video to Text
Real-world videos often have complex dynamics; and methods for generating
open-domain video descriptions should be sensitive to temporal structure and
allow both input (sequence of frames) and output (sequence of words) of
variable length. To approach this problem, we propose a novel end-to-end
sequence-to-sequence model to generate captions for videos. For this we exploit
recurrent neural networks, specifically LSTMs, which have demonstrated
state-of-the-art performance in image caption generation. Our LSTM model is
trained on video-sentence pairs and learns to associate a sequence of video
frames to a sequence of words in order to generate a description of the event
in the video clip. Our model naturally is able to learn the temporal structure
of the sequence of frames as well as the sequence model of the generated
sentences, i.e. a language model. We evaluate several variants of our model
that exploit different visual features on a standard set of YouTube videos and
two movie description datasets (M-VAD and MPII-MD).Comment: ICCV 2015 camera-ready. Includes code, project page and LSMDC
challenge result
FastDepth: Fast Monocular Depth Estimation on Embedded Systems
Depth sensing is a critical function for robotic tasks such as localization,
mapping and obstacle detection. There has been a significant and growing
interest in depth estimation from a single RGB image, due to the relatively low
cost and size of monocular cameras. However, state-of-the-art single-view depth
estimation algorithms are based on fairly complex deep neural networks that are
too slow for real-time inference on an embedded platform, for instance, mounted
on a micro aerial vehicle. In this paper, we address the problem of fast depth
estimation on embedded systems. We propose an efficient and lightweight
encoder-decoder network architecture and apply network pruning to further
reduce computational complexity and latency. In particular, we focus on the
design of a low-latency decoder. Our methodology demonstrates that it is
possible to achieve similar accuracy as prior work on depth estimation, but at
inference speeds that are an order of magnitude faster. Our proposed network,
FastDepth, runs at 178 fps on an NVIDIA Jetson TX2 GPU and at 27 fps when using
only the TX2 CPU, with active power consumption under 10 W. FastDepth achieves
close to state-of-the-art accuracy on the NYU Depth v2 dataset. To the best of
the authors' knowledge, this paper demonstrates real-time monocular depth
estimation using a deep neural network with the lowest latency and highest
throughput on an embedded platform that can be carried by a micro aerial
vehicle.Comment: Accepted for presentation at ICRA 2019. 8 pages, 6 figures, 7 table
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