2,144 research outputs found
Real time multiple camera person detection and tracking
As the amount of video data grows larger every day, the efforts to create intelligent
systems able to perceive, understand and extrapolate useful information from this
data grow larger, namely object detection and tracking systems have been a widely
researched area in the past few years. In the present work we develop a real time,
multiple camera, multiple person detection and tracking system prototype, using
static, overlapped, sh-eye top view cameras. The goal is to create a system able
to intelligently and automatically extrapolate object trajectories from surveillance
footage. To solve these problems we employ different types of techniques, namely
a combination of the representational power of deep neural networks, which have
been yielding outstanding results in computer vision problems over the last few
years, and more classical, already established object tracking algorithms in order
to represent and track the target objects. In particular, we split the problem in
two sub-problems: single camera multiple object tracking and multiple camera
multiple object tracking, which we tackle in a modular manner. Our long-term
motivation is to deploy this system in a commercial application, such as commercial
areas or airports, so that we can build upon intelligent visual surveillance systems.À medida que a quantidade de dados de vídeo cresce, os esforços para criar sistemas
inteligentes capazes de observar, entender e extrapolar informação útil destes dados
intensifcam-se. Nomeadamente, sistemas de detecção e tracking de objectos têm
sido uma àrea amplamente investigada nos últimos anos. No presente trabalho,
desenvolvemos um protótipo de tracking multi-câmara, multi-objecto que corre em
tempo real, e que usa várias câmaras fish-eye estáticas de topo, com sobreposição
entre elas. O objetivo é criar um sistema capaz de extrapolar de modo inteligente
e automático as trajetórias de pessoas a partir de imagens de vigilância. Para
resolver estes problemas, utilizamos diferentes tipos de técnicas, nomeadamente,
uma combinação do poder representacional das redes neurais, que têm produzido
excelentes resultados em problemas de visão computacional nos últimos anos, e
algoritmos de tracking mais clássicos e já estabelecidos, para representar e seguir o
percurso dos objectos de interesse. Em particular, dividimos o problema maior em
dois sub-problemas: tracking de objetos de uma única câmera e tracking de objetos
de múltiplas câmeras, que abordamos de modo modular. A nossa motivação a
longo prazo é implmentar este tipo de sistema em aplicações comerciais, como
áreas comerciais ou aeroportos, para que possamos dar mais um passo em direcção
a sistemas de vigilância visual inteligentes
Semantically Guided Depth Upsampling
We present a novel method for accurate and efficient up- sampling of sparse
depth data, guided by high-resolution imagery. Our approach goes beyond the use
of intensity cues only and additionally exploits object boundary cues through
structured edge detection and semantic scene labeling for guidance. Both cues
are combined within a geodesic distance measure that allows for
boundary-preserving depth in- terpolation while utilizing local context. We
model the observed scene structure by locally planar elements and formulate the
upsampling task as a global energy minimization problem. Our method determines
glob- ally consistent solutions and preserves fine details and sharp depth
bound- aries. In our experiments on several public datasets at different levels
of application, we demonstrate superior performance of our approach over the
state-of-the-art, even for very sparse measurements.Comment: German Conference on Pattern Recognition 2016 (Oral
Rethinking the Inception Architecture for Computer Vision
Convolutional networks are at the core of most state-of-the-art computer
vision solutions for a wide variety of tasks. Since 2014 very deep
convolutional networks started to become mainstream, yielding substantial gains
in various benchmarks. Although increased model size and computational cost
tend to translate to immediate quality gains for most tasks (as long as enough
labeled data is provided for training), computational efficiency and low
parameter count are still enabling factors for various use cases such as mobile
vision and big-data scenarios. Here we explore ways to scale up networks in
ways that aim at utilizing the added computation as efficiently as possible by
suitably factorized convolutions and aggressive regularization. We benchmark
our methods on the ILSVRC 2012 classification challenge validation set
demonstrate substantial gains over the state of the art: 21.2% top-1 and 5.6%
top-5 error for single frame evaluation using a network with a computational
cost of 5 billion multiply-adds per inference and with using less than 25
million parameters. With an ensemble of 4 models and multi-crop evaluation, we
report 3.5% top-5 error on the validation set (3.6% error on the test set) and
17.3% top-1 error on the validation set
Object Tracking in Vary Lighting Conditions for Fog based Intelligent Surveillance of Public Spaces
With rapid development of computer vision and artificial intelligence, cities are becoming more and more intelligent. Recently, since intelligent surveillance was applied in all kind of smart city services, object tracking attracted more attention. However, two serious problems blocked development of visual tracking in real applications. The first problem is its lower performance under intense illumination variation while the second issue is its slow speed. This paper addressed these two problems by proposing a correlation filter based tracker. Fog computing platform was deployed to accelerate the proposed tracking approach. The tracker was constructed by multiple positions' detections and alternate templates (MPAT). The detection position was repositioned according to the estimated speed of target by optical flow method, and the alternate template was stored with a template update mechanism, which were all computed at the edge. Experimental results on large-scale public benchmark datasets showed the effectiveness of the proposed method in comparison with state-of-the-art methods
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