论文标题
通过同时分类和跟踪的有效，准确的对象检测
Efficient and accurate object detection with simultaneous classification and tracking
论文作者
论文摘要
与环境（例如对象检测和跟踪）相互作用是移动机器人的关键能力。除了高精度外，还需要在处理工作和能耗方面的效率。为了满足这两个要求，我们提出了一个基于点流中同时分类和跟踪的检测框架。在此框架中，跟踪器以点云的序列执行数据关联，从而引导检测器避免冗余处理（即对已经知道的对象进行分类）。对于分类不够确定的对象，融合模型旨在融合所选的关键观测值，这些观察值在整个跟踪范围内提供不同的观点。因此，可以提高性能（准确性和检测效率）。此方法特别适合检测和跟踪移动对象，如果使用常规程序解决，则需要昂贵的计算过程。实验是在基准数据集上进行的，结果表明，所提出的方法在效率和准确性方面均优于原始跟踪方法。
Interacting with the environment, such as object detection and tracking, is a crucial ability of mobile robots. Besides high accuracy, efficiency in terms of processing effort and energy consumption are also desirable. To satisfy both requirements, we propose a detection framework based on simultaneous classification and tracking in the point stream. In this framework, a tracker performs data association in sequences of the point cloud, guiding the detector to avoid redundant processing (i.e. classifying already-known objects). For objects whose classification is not sufficiently certain, a fusion model is designed to fuse selected key observations that provide different perspectives across the tracking span. Therefore, performance (accuracy and efficiency of detection) can be enhanced. This method is particularly suitable for detecting and tracking moving objects, a process that would require expensive computations if solved using conventional procedures. Experiments were conducted on the benchmark dataset, and the results showed that the proposed method outperforms original tracking-by-detection approaches in both efficiency and accuracy.
