Towards Automatic Wild Animal Detection in Low Quality Camera-Trap Images Using Two-Channeled Perceiving Residual Pyramid Networks

Monitoring animals in the wild without disturbing them is possible using camera trapping framework, which is a technique to study wildlife using automatically triggered cameras and produces great volumes of data. However, camera trapping collects images often result in low image quality and includes a lot of false positives (images without animals), which must be detection before the postprocessing step. This paper presents a two-channeled perceiving residual pyramid networks (TPRPN) for camera-trap images objection. Our TPRPN model attends to generating high-resolution and high-quality results. In order to provide enough local information, we extract depth cue from the original images and use two-channeled perceiving model as input to training our networks. Finally, the proposed three-layer residual blocks learn to merge all the information and generate full size detection results. Besides, we construct a new high-quality dataset with the help of Wildlife Thailand's Community and eMammal Organization. Experimental results on our dataset demonstrate that our method is superior to the existing object detection methods.