2,881 research outputs found
Deep Poselets for Human Detection
We address the problem of detecting people in natural scenes using a part
approach based on poselets. We propose a bootstrapping method that allows us to
collect millions of weakly labeled examples for each poselet type. We use these
examples to train a Convolutional Neural Net to discriminate different poselet
types and separate them from the background class. We then use the trained CNN
as a way to represent poselet patches with a Pose Discriminative Feature (PDF)
vector -- a compact 256-dimensional feature vector that is effective at
discriminating pose from appearance. We train the poselet model on top of PDF
features and combine them with object-level CNNs for detection and bounding box
prediction. The resulting model leads to state-of-the-art performance for human
detection on the PASCAL datasets
Real-time human action recognition on an embedded, reconfigurable video processing architecture
Copyright @ 2008 Springer-Verlag.In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine (SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. āmotion history imageā) class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfiured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments.DTI and Broadcom Ltd
FPGA implementation of real-time human motion recognition on a reconfigurable video processing architecture
In recent years, automatic human motion recognition has been widely researched within the computer vision and image processing communities. Here we propose a real-time embedded vision solution for human motion recognition implemented on a ubiquitous device. There are three main contributions in this paper. Firstly, we have developed a fast human motion recognition system with simple motion features and a linear Support Vector Machine(SVM) classifier. The method has been tested on a large, public human action dataset and achieved competitive performance for the temporal template (eg. ``motion history image") class of approaches. Secondly, we have developed a reconfigurable, FPGA based video processing architecture. One advantage of this architecture is that the system processing performance can be reconfigured for a particular application, with the addition of new or replicated processing cores. Finally, we have successfully implemented a human motion recognition system on this reconfigurable architecture. With a small number of human actions (hand gestures), this stand-alone system is performing reliably, with an 80% average recognition rate using limited training data. This type of system has applications in security systems, man-machine communications and intelligent environments
Face Attribute Prediction Using Off-the-Shelf CNN Features
Predicting attributes from face images in the wild is a challenging computer
vision problem. To automatically describe face attributes from face containing
images, traditionally one needs to cascade three technical blocks --- face
localization, facial descriptor construction, and attribute classification ---
in a pipeline. As a typical classification problem, face attribute prediction
has been addressed using deep learning. Current state-of-the-art performance
was achieved by using two cascaded Convolutional Neural Networks (CNNs), which
were specifically trained to learn face localization and attribute description.
In this paper, we experiment with an alternative way of employing the power of
deep representations from CNNs. Combining with conventional face localization
techniques, we use off-the-shelf architectures trained for face recognition to
build facial descriptors. Recognizing that the describable face attributes are
diverse, our face descriptors are constructed from different levels of the CNNs
for different attributes to best facilitate face attribute prediction.
Experiments on two large datasets, LFWA and CelebA, show that our approach is
entirely comparable to the state-of-the-art. Our findings not only demonstrate
an efficient face attribute prediction approach, but also raise an important
question: how to leverage the power of off-the-shelf CNN representations for
novel tasks.Comment: In proceeding of 2016 International Conference on Biometrics (ICB
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