1,409 research outputs found
Learning boosted asymmetric classifiers for object detection
http://ieeexplore.ieee.orgObject detection can be posted as those classification tasks where the rare positive patterns are to be distinguished from the enormous negative patterns. To avoid the danger of missing positive patterns, more attention should be payed on them. Therefore there should be different requirements for False Reject Rate (FRR) and False Accept Rate (FAR) , and learning a classifier should use an asymmetric factor to balance between FRR and FAR. In this paper, a normalized asymmetric classification error is proposed for the task of rejecting negative patterns. Minimizing it not only controls the ratio of FRR and FAR, but more importantly limits the upper-bound of FRR. The latter characteristic is advantageous for those tasks where there is a requirement for low FRR. Based on this normalized asymmetric classification error, we develop an asymmetric AdaBoost algorithm with variable asymmetric factor and apply it to the learning of cascade classifiers for face detection. Experiments demonstrate that the proposed method achieves less complex classifiers and better performance than some previous AdaBoost methods
Asymmetric Pruning for Learning Cascade Detectors
Cascade classifiers are one of the most important contributions to real-time
object detection. Nonetheless, there are many challenging problems arising in
training cascade detectors. One common issue is that the node classifier is
trained with a symmetric classifier. Having a low misclassification error rate
does not guarantee an optimal node learning goal in cascade classifiers, i.e.,
an extremely high detection rate with a moderate false positive rate. In this
work, we present a new approach to train an effective node classifier in a
cascade detector. The algorithm is based on two key observations: 1) Redundant
weak classifiers can be safely discarded; 2) The final detector should satisfy
the asymmetric learning objective of the cascade architecture. To achieve this,
we separate the classifier training into two steps: finding a pool of
discriminative weak classifiers/features and training the final classifier by
pruning weak classifiers which contribute little to the asymmetric learning
criterion (asymmetric classifier construction). Our model reduction approach
helps accelerate the learning time while achieving the pre-determined learning
objective. Experimental results on both face and car data sets verify the
effectiveness of the proposed algorithm. On the FDDB face data sets, our
approach achieves the state-of-the-art performance, which demonstrates the
advantage of our approach.Comment: 14 page
Asymmetric Totally-corrective Boosting for Real-time Object Detection
Real-time object detection is one of the core problems in computer vision.
The cascade boosting framework proposed by Viola and Jones has become the
standard for this problem. In this framework, the learning goal for each node
is asymmetric, which is required to achieve a high detection rate and a
moderate false positive rate. We develop new boosting algorithms to address
this asymmetric learning problem. We show that our methods explicitly optimize
asymmetric loss objectives in a totally corrective fashion. The methods are
totally corrective in the sense that the coefficients of all selected weak
classifiers are updated at each iteration. In contract, conventional boosting
like AdaBoost is stage-wise in that only the current weak classifier's
coefficient is updated. At the heart of the totally corrective boosting is the
column generation technique. Experiments on face detection show that our
methods outperform the state-of-the-art asymmetric boosting methods.Comment: 14 pages, published in Asian Conf. Computer Vision 201
Accelerated face detector training using the PSL framework
We train a face detection system using the PSL framework [1] which combines the AdaBoost
learning algorithm and Haar-like features. We demonstrate the ability of this framework to
overcome some of the challenges inherent in training classifiers that are structured in cascades
of boosted ensembles (CoBE). The PSL classifiers are compared to the Viola-Jones type cas-
caded classifiers. We establish the ability of the PSL framework to produce classifiers in a
complex domain in significantly reduced time frame. They also comprise of fewer boosted en-
sembles albeit at a price of increased false detection rates on our test dataset. We also report
on results from a more diverse number of experiments carried out on the PSL framework in
order to shed more insight into the effects of variations in its adjustable training parameters
Incremental Training of a Detector Using Online Sparse Eigen-decomposition
The ability to efficiently and accurately detect objects plays a very crucial
role for many computer vision tasks. Recently, offline object detectors have
shown a tremendous success. However, one major drawback of offline techniques
is that a complete set of training data has to be collected beforehand. In
addition, once learned, an offline detector can not make use of newly arriving
data. To alleviate these drawbacks, online learning has been adopted with the
following objectives: (1) the technique should be computationally and storage
efficient; (2) the updated classifier must maintain its high classification
accuracy. In this paper, we propose an effective and efficient framework for
learning an adaptive online greedy sparse linear discriminant analysis (GSLDA)
model. Unlike many existing online boosting detectors, which usually apply
exponential or logistic loss, our online algorithm makes use of LDA's learning
criterion that not only aims to maximize the class-separation criterion but
also incorporates the asymmetrical property of training data distributions. We
provide a better alternative for online boosting algorithms in the context of
training a visual object detector. We demonstrate the robustness and efficiency
of our methods on handwriting digit and face data sets. Our results confirm
that object detection tasks benefit significantly when trained in an online
manner.Comment: 14 page
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features
We propose a simple yet effective approach to the problem of pedestrian
detection which outperforms the current state-of-the-art. Our new features are
built on the basis of low-level visual features and spatial pooling.
Incorporating spatial pooling improves the translational invariance and thus
the robustness of the detection process. We then directly optimise the partial
area under the ROC curve (\pAUC) measure, which concentrates detection
performance in the range of most practical importance. The combination of these
factors leads to a pedestrian detector which outperforms all competitors on all
of the standard benchmark datasets. We advance state-of-the-art results by
lowering the average miss rate from to on the INRIA benchmark,
to on the ETH benchmark, to on the TUD-Brussels
benchmark and to on the Caltech-USA benchmark.Comment: 16 pages. Appearing in Proc. European Conf. Computer Vision (ECCV)
201
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