改进TransTrack多目标生猪行为跟踪方法

Methods for multi-target tracking of pig action using improved TransTrack

高效准确地监测群养生猪的行为变化以获取其生理、健康和福利状况,对于实现生猪智能精细化养殖具有重要意义。针对猪场自然场景下光照变化和猪只粘连遮挡等因素影响,使得猪只行为跟踪中存在误检、漏检和身份频繁错误变换问题,该研究提出一种改进的TransTrack多目标生猪行为跟踪方法。首先,在目标检测模块中,采用改进的并集交并比的匹配算法,去除猪只遮挡导致的目标误检检测框。然后,在跟踪模块中,根据高低匹配阈值进行2次数据关联,提高光照变化下漏检目标的跟踪准确性。最后,针对误检与漏检导致跟踪中猪只身份错误变换,根据猪栏中猪只数量信息,限制猪只身份编号值的错误增加,提高猪只身份准确识别率。在公开数据集和私有数据集上的试验结果表明,改进的TransTrack在多目标跟踪准确率(multiple object tracking accuracy,MOTA),高阶跟踪准确率(higher order tracking accuracy,HOTA)和身份变换(identity switches,IDs)分别为92.0%、69.8%和210。在公开数据集中,对比Trackformer,JDE和TransTrack模型,改进的TransTrack方法在MOTA分别提高3.9,9.0和13.1个百分点,HOTA分别提高1.3,9.5和8.3个百分点,IDs分别降低136,326和376。在私有数据集中,对比Trackformer和TransTrack模型,改进的TransTrack方法在MOTA分别提高14.4和15.8个百分点,HOTA分别提高1.8和9.5个百分点。结果显示,改进的TransTrack方法能够更加稳定地实现对群养生猪的行为跟踪,为群养生猪行为识别与智能分析提供技术支持。

Pig production is dominated in the livestock industry,particularly for the food safety,social stability,and the coordinated development of the national economy.An accurate tracking of pig behavior can greatly contribute to the health and well-being of pigs,in terms of the detection of abnormal conditions,such as diseases and dangerous movements.However,manual monitoring cannot fully meet the large-scale production in recent years,due to the time consuming,subjective,and labor intensity.Fortunately,the video surveillance technology has been carried out to detect and track pigs.But it is still lacking on accurate tracking and detection of pig behavior in various complex scenes(day or night,sparse or dense condition).In this study,an improved TransTrak multiple object tracking(MOT)was proposed to automatically detect the pigs,and then track the behavior of each detected pig,with considering the motion information of the behavior.Three improvements included an improved CIOU matching to remove the overlapping detections,the behavior category learning with MOT,and the data association.Therefore,the improved TransTrack approach performed better to identify the pig objects during behavior tracking in complex scenarios.The improved TransTrack was validated on the special dataset under a variety of settings.The specific dataset included the public dataset with 23 video sequences and 6900 images,and the private dataset with 8 video segments and 2400 images.The experimental results show that the MOT accuracy(MOTA),the higher order tracking accuracy(HOTA),and ID switches(IDs)on all test videos were 92.0%,69.8%,and 210,respectively,using the improved TransTrack.On the public dataset,the improved TransTrack achieved the MOTA with 92.4%,HOTA with 72.1%,and IDs with 147.And the improved TransTrack improved the MOTA by 3.9,9.0 and 13.1 percentage points,HOTA by 1.3,9.5,and 8.3 percentage points,and decreased in the IDs by 136,326,and 376,respectively,compared with the Trackformer,JDE,and TransTrack.On the private dataset,the improved TransTrack obtained the MOTA with 91.5%,HOTA with 62.5%,and IDs with 63.The MOTA was increased by 14.4,and 15.8 percentage points,and the HOTA was improved by 1.8,and 9.5 percentage points,respectively,compared with the Trackformer and TransTrack.The improved TransTrack can be expected to obtain the best performance in the tracking evaluation metrics(MOTA,HOTA,and IDs)among the four methods.Therefore,the improved TransTrack can also provide the scalable technical support for the automatic monitoring pigs.