The proposed method obtains the best results on all test sets compared to five other MOT algorithms including DeepSORT, StrongSORT, ByteTrack, OC-SORT, and BoT-SORT. In the 10-minute and 1-hour test sets, the proposed method achieves 70.28 and 72.24, 63.3 % and 76.4 %, 94.1 % and ...
StrongSORT 57.8 85.4 67.5 379 9.6 ByteTrack 65.0 83.7 82.5 90 17.1 OC-SORT 74.8 93.7 93.1 17 17.4 BoT-SORT 76.7 96.1 91.3 55 19.3 BoT-SORT-Slim 78.7 97.0 93.4 105 71.2 In terms of tracking performance, BoT-SORT-Slim achieves a HOTA of 84.7 %, MOTA of 98.3 %, IDF1 of 98.4 %, ...
1093 -- 1:06 App YOLOv7 + Strong Sort在高速场景视频上表现超赞 1090 44 14:03:43 App 刷爆!【CV项目实战—智慧交通】翻遍全网终于找到了这么强的CV项目实战教程,带你秒变大神!——(多目标跟踪、卡尔曼滤波、匈牙利算法、车流量统计、车道线提取) 29.1万 187 1:39 App 总台记者探访石家庄公共场所 ...
Configure a basic example of how to integrate yolov10 with many SOTA tracker modules with the help of BoxMOT library
Strong SORT:python tracker/track.py --dataset uavdt --detector yolov8 --tracker strongsort --kalman_format strongsort --detector_model_path weights/yolov8l_UAVDT_60epochs_20230509.pt Sparse Track:python tracker/track.py --dataset uavdt --detector yolov8 --tracker sparsetrack --kalman_format ...
However, there were no significant differences of AP in terms of the different scenes, which proved that the model has strong adaptability to different scenes. Table 2. The performance of our method under different scenes. 3.2. Comparison of Different Lightweight Models In this section, to ...
YOLOV7 + StrongSORT 实现目标检测与跟踪,基于 OSNet 环境windows 10 64bit python 3.8 pytorch1.7.1 + cu101 视频看这里 Youtube Bilibili 简介前面,我们介绍过 基于YOLOv5…… 08-16 立刻查看 YOLOv7实例分割 环境ubuntu 18.04 64bit python 3.8 pytorch1.8.2 + cu111 视频看这里 Youtube Bilibili 简介...
25、基于上述方案,本发明提供了一种基于改进yolov7模型的柑橘青果跟踪计数方法及系统,以yolov7s为基准的目标检测模型,利用常春藤智能优化算法对yolov7s模型的超参数进行优化,提升其检测精度;然后将此优化后的模型结合改进后的strongsort目标跟踪算法ostrongsort,移植至边缘平台中,形成便携式移动柑橘青果跟踪计数设备;通过...
However, there were no significant differences of AP in terms of the different scenes, which proved that the model has strong adaptability to different scenes. Table 2. The performance of our method under different scenes. 3.2. Comparison of Different Lightweight Models In this section, to ...
2024/05/31: Thanks to mohamedsamirx for the integration with BoTSORT, DeepOCSORT, OCSORT, HybridSORT, ByteTrack, StrongSORT using BoxMOT library! 2024/05/31: Thanks to kaylorchen for the integration with rk3588! 2024/05/30: Thanks to eaidova for the integration with OpenVINO™! 2024...