This paper considers the problem of detecting and tracking objects in a sequence of images. The problem is formulated in a filtering framework, using the output of object-detection algorithms as measurements. An extension to the filtering formulation is proposed that incorporates class information from the previous frame to robustify the classification, even if the object-detection algorithm outputs an incorrect prediction. Further, the properties of the object-detection algorithm are exploited to quantify the uncertainty of the bounding box detection in each frame. The complete filtering method is evaluated on camera trap images of the four large Swedish carnivores, bear, lynx, wolf, and wolverine. The experiments show that the class tracking formulation leads to a more robust classification.

该论文考虑了在图像序列中检测和跟踪对象的问题，使用对象检测算法的输出作为测量，提出了一种扩展的过滤公式，将前一帧的类别信息纳入以增强分类的鲁棒性，即使对象检测算法输出错误的预测。此外，利用对象检测算法的属性量化了每帧中界定框检测的不确定性，该完整的过滤方法在瑞典四种大型食肉动物（熊、猞猁、狼和狼）的照相机陷阱图像上进行了评估，实验结果表明类别跟踪公式可以得到更强大的分类。

利用机器学习软件进行扩展目标跟踪--应用于动物分类