Visual object recognition plays an important role in the fields of computer vision and robotics. Static analysis of an image from a single viewpoint may not contain enough information to recognize an object unambiguously. Active object recognition (AOR) is aimed at collecting additional information to reduce ambiguity by purposefully adjusting the viewpoint of an observer. Existing AOR methods are oriented to a single task whose goal is to recognize an object by the minimum number of viewpoints. This paper presents a novel framework to deal with multiple AOR tasks based on feature decision tree (FDT). In the framework, in the light of the distribution of predetermined features on each object in a model base, a prior feature distribution table is firstly created as a kind of prior knowledge. Then it is utilized for the construction of FDT which describes the transition process of recognition states when different viewpoints are selected. Finally, in order to determine the next best viewpoints for the tasks with different goals, a unified optimization problem is established and solved by tree dynamic programming algorithm. In addition, the existing evaluation method of viewpoint planning (VP) efficiency is improved. According to whether the prior probability of the appearance of each object is known, the VP efficiency of different tasks is evaluated respectively. Experiments on the simulation and real environment show that the proposed framework obtains rather promising results in different AOR tasks.

视觉对象识别在计算机视觉和机器人技术领域发挥着重要作用。从单个视点对图像的静态分析可能不包含足够的信息来明确识别对象。主动对象识别 (AOR) 旨在通过有目的地调整观察者的视角来收集额外信息以减少歧义。现有的 AOR 方法面向单个任务，其目标是通过最少数量的视点识别对象。本文提出了一种新的框架来处理基于特征决策树（FDT）的多个 AOR 任务。在该框架中，根据模型库中每个对象的预定特征分布，首先创建先验特征分布表作为一种先验知识。然后用于构建FDT，描述选择不同视点时识别状态的转换过程。最后，为了为不同目标的任务确定下一个最佳视点，建立统一的优化问题并通过树动态规划算法求解。此外，改进了现有的视点规划（VP）效率评估方法。根据每个物体出现的先验概率是否已知，分别评估不同任务的VP效率。模拟和真实环境的实验表明，所提出的框架在不同的 AOR 任务中获得了相当有希望的结果。为了为不同目标的任务确定下一个最佳视点，建立统一的优化问题并通过树动态规划算法求解。此外，改进了现有的视点规划（VP）效率评估方法。根据每个物体出现的先验概率是否已知，分别评估不同任务的VP效率。模拟和真实环境的实验表明，所提出的框架在不同的 AOR 任务中获得了相当有希望的结果。为了为不同目标的任务确定下一个最佳视点，建立统一的优化问题并通过树动态规划算法求解。此外，改进了现有的视点规划（VP）效率评估方法。根据每个物体出现的先验概率是否已知，分别评估不同任务的VP效率。模拟和真实环境的实验表明，所提出的框架在不同的 AOR 任务中获得了相当有希望的结果。根据每个物体出现的先验概率是否已知，分别评估不同任务的VP效率。模拟和真实环境的实验表明，所提出的框架在不同的 AOR 任务中获得了相当有希望的结果。根据每个物体出现的先验概率是否已知，分别评估不同任务的VP效率。模拟和真实环境的实验表明，所提出的框架在不同的 AOR 任务中获得了相当有希望的结果。