Real-time Face Detection and Tracking of Animals
Real-time Face Detection and Tracking of Animals
复制标题
动物的实时人脸检测和跟踪
DOI：
10.1109/neurel.2006.341167
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
J. Calic
中科院分区：
文献类型：
--
作者：
T. Burghardt;J. Calic
提出了一种通过检测和跟踪动物的人脸来实时提取野生动物视频中动物的运动活动信息的方法。作为一个示例应用程序，该系统以狮子为例进行训练。基本的检测策略基于Viola-Jones检测器中使用的概念，Viola-Jones检测器最初用于人脸检测，使用类似Haar的特征和AdaBoost分类器。通过将检测算法与低层特征跟踪器相结合，实现了平滑、准确的跟踪。使用基于时间置信度累积动态估计动物实际存在的可能性的特定相干模型来确保可靠且在时间上连续的检测/跟踪能力。跟踪器生成的信息可用于自动分类和注释野生动物视频存储库中的基本机车行为
This paper presents a real-time method for extracting information about the locomotive activity of animals in wildlife videos by detecting and tracking the animals' faces. As an example application, the system is trained on lions. The underlying detection strategy is based on the concepts used in the Viola-Jones detector, an algorithm that was originally used for human face detection utilising Haar-like features and AdaBoost classifiers. Smooth and accurate tracking is achieved by integrating the detection algorithm with a low-level feature tracker. A specific coherence model that dynamically estimates the likelihood of the actual presence of an animal based on temporal confidence accumulation is employed to ensure a reliable and temporally continuous detection/tracking capability. The information generated by the tracker can be used to automatically classify and annotate basic locomotive behaviours in wildlife video repositories