Animal Detection From Highly Cluttered Natural Scenes Using Spatiotemporal Object Region Proposals and Patch Verification
Animal Detection From Highly Cluttered Natural Scenes Using Spatiotemporal Object Region Proposals and Patch Verification
复制标题
使用时空对象区域建议和补丁验证从高度混乱的自然场景中进行动物检测
DOI：
10.1109/tmm.2016.2594138
复制
发表时间：
2016-10-01
影响因子：
7.3
通讯作者：
Cao, Wenming
中科院分区：
文献类型：
--
作者：
Zhang, Zhi;He, Zhihai;Cao, Wenming
在本文中，我们考虑的动物目标检测和分割的野生动物监测视频的运动触发相机，称为相机陷阱。对于这些类型的视频，现有的方法往往遭受低检测率，由于前景动物和杂乱的背景之间的低对比度，以及高误报率，由于动态背景。为了解决这个问题，我们首先开发了一种新的方法来生成动物对象区域的建议，在时空域中使用多级图切割。然后，我们开发了一种跨帧时间补丁验证方法来确定这些区域提议是真实的动物还是背景补丁。我们使用联合深度学习和Fisher向量编码的定向梯度特征直方图构建了一个有效的动物检测特征描述。我们对一组具有挑战性的相机陷阱数据进行了广泛的实验结果和性能比较，结果表明，所提出的时空对象建议和补丁验证框架在动物对象检测准确性方面优于最先进的方法，包括最近的Faster-RCNN方法，高达4.5%。
In this paper, we consider the animal object detection and segmentation from wildlife monitoring videos captured by motion-triggered cameras, called camera-traps. For these types of videos, existing approaches often suffer from low detection rates due to low contrast between the foreground animals and the cluttered background, as well as high false positive rates due to the dynamic background. To address this issue, we first develop a new approach to generate animal object region proposals using multilevel graph cut in the spatiotemporal domain. We then develop a cross-frame temporal patch verification method to determine if these region proposals are true animals or background patches. We construct an efficient feature description for animal detection using joint deep learning and histogram of oriented gradient features encoded with Fisher vectors. Our extensive experimental results and performance comparisons over a diverse set of challenging camera-trap data demonstrate that the proposed spatiotemporal object proposal and patch verification framework outperforms the state-of-the-art methods, including the recent Faster-RCNN method, on animal object detection accuracy by up to 4.5%.