7 research outputs found
Learning Complexity-Aware Cascades for Deep Pedestrian Detection
The design of complexity-aware cascaded detectors, combining features of very
different complexities, is considered. A new cascade design procedure is
introduced, by formulating cascade learning as the Lagrangian optimization of a
risk that accounts for both accuracy and complexity. A boosting algorithm,
denoted as complexity aware cascade training (CompACT), is then derived to
solve this optimization. CompACT cascades are shown to seek an optimal
trade-off between accuracy and complexity by pushing features of higher
complexity to the later cascade stages, where only a few difficult candidate
patches remain to be classified. This enables the use of features of vastly
different complexities in a single detector. In result, the feature pool can be
expanded to features previously impractical for cascade design, such as the
responses of a deep convolutional neural network (CNN). This is demonstrated
through the design of a pedestrian detector with a pool of features whose
complexities span orders of magnitude. The resulting cascade generalizes the
combination of a CNN with an object proposal mechanism: rather than a
pre-processing stage, CompACT cascades seamlessly integrate CNNs in their
stages. This enables state of the art performance on the Caltech and KITTI
datasets, at fairly fast speeds
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Towards Universal Object Detection
Object detection is one of the most important and challenging research topics in computer vision. It is playing an important role in our everyday life and has many applications, e.g. surveillance, autonomous driving, robotics, drone, medical imaging, etc. The ultimate goal of object detection is a universal object detector that can work very well in any case under any condition like human vision system. However, there are multiple challenges on the universality of object detection, e.g. scale-variance, high-quality requirement, domain shift, computational constraint, etc. These will prevent the object detector from being widely used for various scales of objects, critical applications requiring extremely accurate localization, scenarios with changing domain priors, and diverse hardware settings. To address these challenges, multiple solutions have been proposed in this thesis. These include an efficient multi-scale architecture to achieve scale-invariant detection, a robust multi-stage framework effective for high-quality requirement, a cross-domain solution to extend the universality over various domains, and a design of complexity-aware cascades and a novel low-precision network to enhance the universality under different computational constraints. All these efforts have substantially improved the universality of object detection, and the advanced object detector can be applied to broader environments
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Boosting algorithms for detector cascade learning
The problem of learning classifier cascades is considered. A new cascade boosting algorithm, fast cascade boosting (FCBoost), is proposed. FCBoost is shown to have a number of interesting properties, namely that it 1) minimizes a Lagrangian risk that jointly accounts for classification accuracy and speed, 2) generalizes adaboost, 3) can be made cost-sensitive to support the design of high detection rate cascades, and 4) is compatible with many predictor structures suitable for sequential decision making. It is shown that a rich family of such structures can be derived recursively from cascade predictors of two stages, denoted cascade generators. Generators are then proposed for two new cascade families, last-stage and multiplicative cascades, that generalize the two most popular cascade architectures in the literature. The concept of neutral predictors is finally introduced, enabling FCBoost to automatically determine the cascade conffguration, i.e., number of stages and number of weak learners per stage, for the learned cascades. Experiments on face and pedestrian detection show that the resulting cascades outperform current state-of-the-art methods in both detection accuracy and speed. © 2014 Mohammad Saberian and Nuno Vasconcelos
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Boosting algorithms for detector cascade learning
The problem of learning classifier cascades is considered. A new cascade boosting algorithm, fast cascade boosting (FCBoost), is proposed. FCBoost is shown to have a number of interesting properties, namely that it 1) minimizes a Lagrangian risk that jointly accounts for classification accuracy and speed, 2) generalizes adaboost, 3) can be made cost-sensitive to support the design of high detection rate cascades, and 4) is compatible with many predictor structures suitable for sequential decision making. It is shown that a rich family of such structures can be derived recursively from cascade predictors of two stages, denoted cascade generators. Generators are then proposed for two new cascade families, last-stage and multiplicative cascades, that generalize the two most popular cascade architectures in the literature. The concept of neutral predictors is finally introduced, enabling FCBoost to automatically determine the cascade conffguration, i.e., number of stages and number of weak learners per stage, for the learned cascades. Experiments on face and pedestrian detection show that the resulting cascades outperform current state-of-the-art methods in both detection accuracy and speed. © 2014 Mohammad Saberian and Nuno Vasconcelos