For most wild animals, the peak activity occurs during the night hours—their observation is possible only with the use of specialized equipment. Advancements in technology and the availability of thermal sensing devices allow researchers to examine wildlife. This study compares different methods for animal detection in thermal camera images including classical (HOG/SVM) and based on deep neural networks (Faster RCNN and YOLO). A comparison was performed to indicate the most beneficial mean Average Precision (mAP) for different levels of Intersection over Union (IoU) coverage thresholds and sensitivity (Recall). The results were evaluated on a scratch dataset containing two animal families (Cervidae and Suidae). The models were compared in terms of precision, recall, and training time. After a series of tests, the achieved performance was very satisfying: for the YOLOv3 network, the obtained mAP was above 90% for IoU > 50%; for Faster R-CNN, the obtained mAP was 87%; and for HOG/SVM, the obtained mAP was 40%. The training time for HOG/SVM was under 1 min, for YOLOv3 it was around 50 min, and for Faster R-CNN it was around 60 min. This research provides an important starting point for the creation of a ground-based system for autonomous observation of migration and population numbers, which is a unique approach in the field of wildlife research.