Camera‐trapping is widespread in wildlife studies, especially for species with individually unique markings to which capture–recapture analytical techniques can be applied. The large volume of data such studies produce have encouraged researchers to increasingly look to computer‐assisted pattern‐recognition software to expedite individual identifications, but little work has been done to formally assess such software for camera‐trap data. We used 2 sets of camera‐trap images—359 images of jaguars (Panthera onca) and 332 images of ocelots (Leopardus pardalis) collected from camera traps deployed in 4 study sites in Orange Walk District, Belize, in 2015 and 2016—to compare the accuracy of 2 such programs, HotSpotter and Wild‐ID, and assess the effect of image quality on matching success. Overall, HotSpotter selected a correct match as its top rank 71–82% of the time, whereas the rate for Wild‐ID was 58–73%. Positive matching rates for both programs were highest for high‐quality images (85–99%) and lowest for low‐quality images (28–52%). False match rates were very low for HotSpotter (0–2%) but these were greater in Wild‐ID (6–28%). When lower ranks were also considered, both programs performed similarly (overall 22–24% nonmatches for HotSpotter, 17–26% nonmatches for Wild‐ID). We found that in both programs, images more often matched to other images of the same quality; therefore, including multiple reference images of an individual, of different qualities, improves matching success. These programs do not provide fully automatic identification of individuals and human involvement is still required to confirm matches, but we found that they are effective tools to expedite processing of camera‐trap data. We also offer usage recommendations for researchers to maximize the benefits of these tools. © 2020 The Wildlife Society.

中文翻译：

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通过模式识别软件识别美洲虎和豹猫：比较HotSpotter和Wild-ID

相机诱捕技术在野生动植物研究中得到了广泛的应用，尤其是对于具有独特独特标记的物种，可以应用捕获-捕获分析技术。这些研究产生的大量数据鼓励研究人员越来越多地使用计算机辅助的模式识别软件来加快个人识别的速度，但是对于形式上的相机陷印数据正式评估这种软件的工作很少。我们使用了两套相机陷阱图像，分别是359幅美洲虎（Panthera onca）和332只鹰嘴豆（Leopardus pardalis））收集自2015年和2016年在伯利兹奥兰治沃克地区4个研究地点部署的相机陷阱，以比较HotSpotter和Wild-ID这两个程序的准确性，并评估图像质量对匹配成功的影响。总体而言，HotSpotter在71-82％的时间内选择了正确的匹配作为其最高排名，而Wild-ID的匹配率为58-73％。两种程序的正匹配率对于高质量图像最高（85–99％），对于低质量图像最低（28–52％）。HotSpotter的错误匹配率非常低（0–2％），而Wild-ID的错误匹配率则更高（6–28％）。当还考虑到较低的排名时，两个程序的执行情况相似（HotSpotter的总不匹配项为22–24％，Wild-ID的总不匹配项为17–26％）。我们发现，在这两个程序中，图像更经常与其他相同质量的图像匹配。因此，包括个人的不同质量的多个参考图像，可以提高匹配成功率。这些程序无法提供对个人的全自动识别，仍然需要人工参与来确认匹配，但是我们发现它们是加快处理相机陷阱数据的有效工具。我们还为研究人员提供使用建议，以最大程度地利用这些工具。©2020野生动物协会。