The field of animal detection and tracking is moving towards more innovative and automated solutions. Recent developments have focused on improving the accuracy and efficiency of object detection algorithms, as well as integrating them with other technologies such as IoT and computer vision. One notable trend is the use of hybrid approaches that combine different algorithms and techniques to achieve better results. Another area of focus is the development of large-scale datasets and benchmarks for training and evaluating animal detection and tracking models. Noteworthy papers in this area include: A Hybrid YOLOv5-SSD IoT-Based Animal Detection System for Durian Plantation Protection, which presents a comprehensive framework for detecting animals in durian plantations. Web-Scale Collection of Video Data for 4D Animal Reconstruction, which introduces an automated pipeline for collecting and processing large-scale video data for animal reconstruction tasks. Zero-Shot Multi-Animal Tracking in the Wild, which explores the potential of vision foundation models for zero-shot multi-animal tracking. 3EED: Ground Everything Everywhere in 3D, which introduces a multi-platform, multi-modal 3D grounding benchmark for embodied agents. SILVI: Simple Interface for Labeling Video Interactions, which presents an open-source labeling software for annotating interactions in video data.