Improved Wildlife Recognition through Fusing Camera Trap Images and Temporal Metadata

Abstract

Camera traps play an important role in biodiversity monitoring. An increasing number of studies have been conducted to automatically recognize wildlife in camera trap images through deep learning. However, wildlife recognition by camera trap images alone is often limited by the size and quality of the dataset. To address the above issues, we propose the Temporal-SE-ResNet50 network, which aims to improve wildlife recognition accuracy by exploiting the temporal information attached to camera trap images. First, we constructed the SE-ResNet50 network to extract image features. Second, we obtained temporal metadata from camera trap images, and after cyclical encoding, we used a residual multilayer perceptron (MLP) network to obtain temporal features. Finally, the image features and temporal features were fused in wildlife identification by a dynamic MLP module. The experimental results on the Camdeboo dataset show that the accuracy of wildlife recognition after fusing the image and temporal information is about 93.10%, which is an improvement of 0.53%, 0.94%, 1.35%, 2.93%, and 5.98%, respectively, compared with the ResNet50, VGG19, ShuffleNetV2-2.0x, MobileNetV3-L, and ConvNeXt-B models. Furthermore, we demonstrate the effectiveness of the proposed method on different national park camera trap datasets. Our method provides a new idea for fusing animal domain knowledge to further improve the accuracy of wildlife recognition, which can better serve wildlife conservation and ecological research.

Wildlife Real-Time Detection in Complex Forest Scenes Based on YOLOv5s Deep Learning Network

“…In addition to the more popular single-stage and two-stage models such as YOLO and Fast R-CNN, there are also other models that have shown excellent performance in wildlife target detection tasks. For example, Lei Liu et al addressed the issue of accurately recognizing wildlife targets by proposing the Temporal-SE-ResNet50 network [35]. This network not only utilizes ResNet50 to extract image features but also incorporates a residual multilayer perceptron to capture temporal features.…”