483 research outputs found
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
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
Efficiently learning a detection cascade with sparse eigenvectors
Real-time object detection has many computer vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detection system, much effort has been spent on improving the boosting method. In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we introduce greedy sparse linear discriminant analysis (GSLDA) for its conceptual simplicity and computational efficiency; and slightly better detection performance is achieved compared with. Moreover, we propose a new technique, termed boosted greedy sparse linear discriminant analysis (BGSLDA), to efficiently train a detection cascade. BGSLDA exploits the sample reweighting property of boosting and the class-separability criterion of GSLDA. Experiments in the domain of highly skewed data distributions (e.g., face detection) demonstrate that classifiers trained with the proposed BGSLDA outperforms AdaBoost and its variants. This finding provides a significant opportunity to argue that AdaBoost and similar approaches are not the only methods that can achieve high detection results for real-time object detection
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
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