This study presents a lightweight multi-object detection and tracking system tailored for intelligent wildlife monitoring under complex field conditions. To handle background interference, occlusion and limited edge-computing resources, the proposed framework integrates an improved YOLOv8 with ByteTrack. A global attention mechanism (GAM) enhances feature focus and noise suppression, while a lightweight YOLO-based tracking backbone (L-YOLOTrack) reduces model complexity and supports real-time deployment. In the tracking stage, a structure-aware calibration loss (SCALoss) improves localization under occlusion and an enhanced extended Kalman filter (EKF) with adaptive noise adjustment strengthens trajectory continuity under nonlinear motion. Experiments on a wildlife dataset show that mAP@0.5-0.95 increases from 40.1% to 45.7%, multiple object tracking accuracy （MOTA) rises by 14.3%, and parameters decrease by 9.3%. The results demonstrate that the proposed system achieves higher detection accuracy and tracking robustness, offering an efficient and reliable solution for wildlife monitoring.