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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Learning a Family of Detectors via Multiplicative Kernels
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Learning a Family of Detectors via Multiplicative Kernels

机译：通过乘法内核学习检测器系列

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摘要

Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions. Model training is accomplished via standard SVM learning. When the foreground object masks are provided in training, the detectors can also produce object segmentations. A tracking-by-detection framework to recover foreground state in video sequences is also proposed with our model. The advantages of our method are demonstrated on tasks of object detection, view angle estimation, and tracking. Our approach compares favorably to existing methods on hand and vehicle detection tasks. Quantitative tracking results are given on sequences of moving vehicles and human faces.
机译：当对象类表现出较大的类内变化时，对象检测将具有挑战性。在这项工作中，我们表明可以以两个内核函数的乘法形式联合学习前景背景分类（检测）和前景类的类内分类（姿势估计）。通过标准的SVM学习来完成模型训练。在训练中提供前景对象蒙版时，检测器还可以产生对象分割。我们的模型还提出了一种通过检测跟踪来恢复视频序列中前景状态的框架。我们的方法的优势在目标检测，视角估计和跟踪任务上得到了证明。我们的方法与现有的手和车辆检测方法相比具有优势。定量跟踪结果在移动的车辆和人脸的序列上给出。

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