Our paper deals with active multiview object recognition focusing on the directional support of sequential multiple shots. Since inertial sensors are easily available nowadays, we propose the use of them to estimate the orientation change of the camera and thus to estimate the probability of relative poses. With the help of relative orientation change, we can compute transition probabilities between possible poses and can use a hidden Markov model to evaluate state (pose) sequences and can thus increase the recognition rate. Furthermore, we can plan our next viewing position to minimize the risk of misclassification, resulting in higher overall recognition rates. Besides giving the theoretical details, we use two datasets to illustrate the performance of our model through several tests including occlusion, blur, Gaussian noise, and to compare to a solution with a long short-term memory network.

中文翻译：

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具有隐马尔可夫时间支持的主动多视图识别

我们的论文涉及主动多视图对象识别，重点是连续多个镜头的方向支持。由于惯性传感器现在很容易获得，我们建议使用它们来估计相机的方向变化，从而估计相对姿势的概率。在相对方向变化的帮助下，我们可以计算可能姿势之间的转移概率，并可以使用隐马尔可夫模型来评估状态（姿势）序列，从而提高识别率。此外，我们可以规划下一个观看位置，以最大程度地减少误分类的风险，从而提高整体识别率。除了给出理论细节之外，我们还使用两个数据集通过包括遮挡、模糊、高斯噪声在内的多项测试来说明我们模型的性能，