| 基于PigsTrack跟踪器的群养生猪多目标跟踪 |
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| 引用本文： | 张丽雯,周昊,朱启兵.基于PigsTrack跟踪器的群养生猪多目标跟踪[J].农业工程学报,2023,39(16):181-190. |
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| 作者姓名： | 张丽雯 周昊 朱启兵 |
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| 作者单位： | 江南大学物联网工程学院，无锡 214122 |
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| 基金项目： | 江苏省重点研发-现代农业“基于物联网的智慧设施蛋鸡生产技术集成创新与示范”（计划项目：BE2018334） |
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| 摘 要： | 基于视频的生猪行为跟踪和识别对于实现精细化养殖具有重要价值。为了应对群养生猪多目标跟踪任务中由猪只外观相似、遮挡交互等因素带来的挑战,研究提出了基于PigsTrack跟踪器的群养生猪多目标跟踪方法。PigsTrack跟踪器利用高性能YOLOX网络降低目标误检与漏检率,采用Transformer模型获取具有良好区分特性的目标外观特征;基于OC-SORT(observation-centric sort)的思想,通过集成特征匹配、IoU匹配和遮挡恢复匹配策略实现群养生猪的准确跟踪。基于PBVD(pigs behaviours video dataset)数据集的试验结果表明,PigsTrack跟踪器的HOTA(higher order tracking accuracy),MOTA(multiple object tracking accuracy)和IDF1得分(identification F1 score)分别为85.66%、98.59%和99.57%,相较于现有算法的最高精度,分别提高了3.71、0.03和2.05个百分点,证明了PigsTrack跟踪器在解决外观相似和遮挡交互引起...
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| 关 键 词： | 跟踪 检测 群养生猪 PigsTrack跟踪器 遮挡恢复匹配 生猪个体行为统计 |
| 收稿时间： | 2023/4/11 0:00:00 |
| 修稿时间： | 2023/8/10 0:00:00 |
Multi-target tracking of group-housed pigs based on PigsTrack tracker |
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| ZHANG Liwen,ZHOU Hao,ZHU Qibing.Multi-target tracking of group-housed pigs based on PigsTrack tracker[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(16):181-190. |
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| Authors: | ZHANG Liwen ZHOU Hao ZHU Qibing |
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| Institution: | School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China |
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| Abstract: | Abstract: An accurate and rapid identification of pig behavior can play a crucial role in the evaluation of health status and welfare. Individual detection and tracking of pigs can greatly enhance the recognition and statistical analysis of their behaviors in large-scale farming environments. This shift from group to precision management can enable better treatment and care, ultimately leading to the goal of welfare-friendly farming. Nevertheless, the occlusion and poses challenges often remain in the actual scenario of pig farming during object tracking, where the group-raised pigs often overlap and move irregularly with each other. Among them, the current mainstream optimization tracking algorithms (such as ByteTrack) can be used to effectively reduce the missed detection from the occlusion using low-score box matching. But it may increase the false detection. The occlusion recovery matching scheme of the OC-SORT algorithm can be used to significantly reduce the identity changes from the occlusion. However, both algorithms still lack the appearance feature matching, which can easily lead to the target loss in the low frame rate or stuttering videos. The appearance feature matching can be expected to solve occlusion and irregular motion. Unfortunately, the existing convolution-based identity recognition networks often struggle to obtain the appearance features with better discriminatory properties, due to the highly similar appearance of pigs. In this study, a PigsTrack tracker was proposed to further optimize the multi-object tracking for the group-raised pigs in actual scenarios. Firstly, the high-performance detector (YOLOX) was used in the tracker to reduce the false and missed detection rates of pigs in the occluded scenarios. Then, an identity recognition (Re-ID) network was constructed using the Transformer model to extract the appearance features with better discriminative properties. Secondly, the matching strategies of appearance feature, IoU, and occlusion recovery were integrated with the tracker for the accurate tracking of group-raised pigs using the idea of OC-SORT. 20 videos were selected (a total of 36 000 frames) from the original PBVD (Pigs Behaviours Video) dataset. The obtained dataset was labeled using the DarkLabel tool. Furthermore, the main evaluation metrics included HOTA (higher order tracking accuracy), MOTA (multiple object tracking accuracy), and IDF1 (identification F1 score). The comparison showed that the PigsTrack tracker outperformed the rest in all indicators of tracking performance with the HOTA of 85.66%, MOTA of 98.59%, and IDF1 of 99.57 %, which were higher than those of DeepSORT, OC-SORT, ByteTrack and StrongSORT trackers, respectively. Additionally, the three-step matching also performed better stable tracking to reduce the number of false and missed detections, as well as the identity switches, with a total of only 13 identity switches. Overall, the PigsTrack can be suitable for the multi-object tracking of group-reared pigs in the breeding environment, particularly for the pig interactions, occlusions, and similarities in appearance. The visualization of the attention map confirmed that the precise tracking of pigs in long videos was achieved in the feature extractor using the Transformer model. Furthermore, more distinctive appearance features were extracted for more detailed information. The tracking results were compared with and without occlusion recovery matching. As such, the occlusion recovery matching greatly contributed to the tracking pigs in dense environments. Finally, the pig behavior in videos was analyzed using the PigsTrack with the Slowfast network. Experiments demonstrated that superior accuracy, effectiveness, and continuity were achieved to recognize and statistically analyze the daily behaviors of group-reared pigs. In addition, the ABVD dataset was utilized to create Dataset 2 for the supplementary experiments, and then to validate the generalization performance of the model in multi-target tracking tasks of group-raised pigs. The performance of the PigsTrack tracker on Dataset 2 (with HOTA, MOTA, and IDF1 scores of 69.14%, 94.82%, and 90.11%, respectively) outperformed the rest, indicating only a slight gap, compared with the PBVD dataset. Therefore, it is also necessary to further optimize the experimental environment and tracker for the engaged pigs in aggressive behavior. Overall, the PigsTrack can provide an important foundation for precision farming. |
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| Keywords: | tracking detection group-housed pigs PigsTrack tracker occlusion recovery matching individual behavior statistics of pigs |
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