# DenseDuckMOT: A Real-Time Detection-Tracking Coupled Counting Framework for Complex Avicultural Environments

## TL;DR

This paper introduces DenseDuckMOT, a real-time system for accurately counting ducks in crowded barns using lightweight detection and tracking methods.

## Contribution

The novel framework combines an improved detector (DuckNet) and a robust tracker (AKFTrack) for efficient and accurate duck counting in complex environments.

## Key findings

- DenseDuckMOT achieved 98.19% precision and 97.72% recall for duck detection and tracking.
- The AKFTrack tracker outperformed existing methods in crowded and occluded scenes.
- The system supports real-time monitoring with minimal hardware requirements.

## Abstract

Counting ducks in crowded barns is challenging because individuals frequently overlap, move rapidly, and appear blurred in surveillance footage. We propose a lightweight visual perception and online tracking method that detects each Liancheng White Duck and maintains consistent identities across frames for stable, real-time counting. The detector was trained on 2416 annotated images, and the tracking performance was evaluated on five real surveillance videos from a breeding farm. The method achieved 98.19% precision and 97.72% recall with a compact model size of about 4.5 KB, supporting deployment on resource-limited devices. In densely crowded and heavily occluded scenes, the tracking component produced more continuous trajectories and fewer identity mix-ups than commonly used tracking approaches. This work reduces manual workload and unnecessary human disturbance, providing a practical, low-cost monitoring solution for smart duck farming.

The Liancheng White Duck is a nationally protected breed in China, but its high-density farming environment poses significant challenges for target detection and behavior recognition, particularly due to occlusion, motion blur, and flock aggregation, making practical flock monitoring and counting labor intensive and prone to error in real barns. To address these issues, we propose DenseDuckMOT, an integrated detection-tracking framework for practical farm monitoring using existing fixed surveillance cameras with minimal additional hardware cost that combines the improved DuckNet detector with the AKFTrack tracker. DuckNet, based on YOLOv11, incorporates BiFPN, GLSA, and ESDH. It achieves high performance with 98.19% precision, 94.79% mAP@0.75, 97.70% F1-score, and 97.72% recall, while maintaining a lightweight design of only 1.90M parameters and a model size of 4485 KB. AKFTrack introduces adaptive Kalman prediction and a two-stage association scheme. It is evaluated on five dense white duck surveillance videos, where it outperforms or ranks second in MOTA, IDF1, and recall compared to DeepSORT, StrongSORT, and ByteTrack, especially in crowded and occluded scenes. Experimental results, ablation studies, and LayerCAM visualizations confirm the complementary advantages of BiFPN, GLSA, and ESDH, as well as the robustness of AKFTrack in handling occlusion and rapid motion. DenseDuckMOT provides accurate, efficient, and stable real-time monitoring in dynamic poultry farms, offering a scalable solution for intelligent farming.