基于检测和重识别的无人机行人跟踪算法

张嘉辉; 赵威; 王子琛; 蒙志君

doi:10.13700/j.bh.1001-5965.2022.0675

基于检测和重识别的无人机行人跟踪算法

doi: 10.13700/j.bh.1001-5965.2022.0675

北京航空航天大学航空科学与工程学院，北京 100191

基金项目: 国家自然科学基金(61976014)

详细信息

通讯作者:
E-mail：mengzhijun@buaa.edu.cn

中图分类号: V279⁺.2
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- 文章访问数: 256
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- PDF下载量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2022-08-01
- 录用日期: 2022-12-30
- 网络出版日期: 2023-01-19
- 整期出版日期: 2024-08-28

UAV pedestrian tracking algorithm based on detection and re-identification

School of Aeronautic Science and Engineering，Beihang University，Beijing 100191，China

Funds: National Natural Science Foundation of China (61976014)

More Information

Corresponding author: E-mail：mengzhijun@buaa.edu.cn

摘要

摘要:
将智能检测跟踪算法与无人机(UAV)的灵活性相结合是UAV应用的研究热点。针对UAV的视角及运动导致目标滑移和遮挡的问题，提出一种基于检测和重识别的UAV行人跟踪算法。对训练好的YOLOv5进行TensorRT加速，解决UAV计算资源有限的问题；以量化加速的目标检测算法与重识别算法为基础，构建行人跟踪算法框架；设计判定行人匹配度，完成行人匹配系统设计。仿真试验表明：训练后的YOLOv5和OSNet具备一定的精度，采用TensorRT加速后的YOLOv5网络在保证精度的情况下，帧率有了近50%的提升。飞行试验表明：所提算法在行人穿插及障碍物遮挡的情况下，可以实现对目标的稳定跟踪，具备一定的实用性和有效性。
- 无人机 /
- 目标检测 /
- 量化加速 /
- 行人跟踪 /
- 重识别
Abstract:
Combining intelligent detection and tracking algorithms with the flexibility of unmanned aerial vehicle (UAV) is a hot research topic for UAV applications. A UAV pedestrian tracking algorithm based on detection and re-identification was proposed for solving the problems of target slippage and occlusion due to the UAV’s viewpoint and motion. Firstly, TensorRT acceleration of trained YOLOv5 was performed to solve the problem of limited UAV computational resources; secondly, a pedestrian tracking algorithm framework was constructed based on a target detection algorithm and a re-identification algorithm with quantization acceleration; finally, the pedestrian matching degree was designed and determined to complete the pedestrian matching system design. Simulation experiments show that the trained YOLOv5 and OSNet have certain accuracy, and the YOLOv5 network with TensorRT acceleration has nearly 50% improvement in frame rate with guaranteed accuracy. The flight test shows that the proposed algorithm can achieve stable tracking of the target under the situation of pedestrian intersection and obstacle occlusion, and it has certain practicality and effectiveness.
- unmanned aerial vehicle /
- target detection /
- quantization acceleration /
- pedestrian tracking /
- re-identification

HTML全文

图 1 YOLOv5组件构成

Figure 1. Components of YOLOv5

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图 2 YOLOv5-s网络结构

Figure 2. Network structure of YOLOv5-s

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图 3 OSNet构建块

Figure 3. Building block for OSNet

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图 4 孪生结构

Figure 4. Siamese architecture

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图 5 基于检测和重识别的目标跟踪框架

Figure 5. Target tracking framework based on detection and re-identification

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图 6 基于重识别的行人匹配算法结构

Figure 6. Pedestrian matching algorithm structure based on re-identification

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图 7 SiamFC跟踪器遮挡测试结果

Figure 7. Occlusion test results of SiamFC tracker

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图 8 本文算法遮挡测试结果

Figure 8. Occlusion test results of the proposed algorithm

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图 9 无人机飞行试验平台

Figure 9. UAV flight test platform

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图 10 行人跟踪飞行试验

Figure 10. Pedestrian tracking flight test

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图 11 行人干扰飞行试验

Figure 11. Pedestrian interference flight test

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图 12 障碍物遮挡飞行试验

Figure 12. Obstacle occlusion flight test

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表 1 PC端目标检测算法测试结果

Table 1. Test results of target detection algorithm on PC

检测模型正确率召回率 AP@0.5

YOLOv3 0.514 0.383 0.391
YOLOv5-n 0.413 0.297 0.284
YOLOv5-s 0.568 0.373 0.402

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表 2 TensorRT在Jetson Xavier NX模块上的测试结果

Table 2. Test results of TensorRT on Jetson Xavier NX module

深度学习
框架功率/W 在线CPU
个数 Jetson clock AP@0.5 帧率/
(帧·S⁻¹)

Pytorch 15 2 开启 0.402 24.8

TensorRT 15 2 开启 0.401 37.4

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表 3 PC端re-id算法测试结果

Table 3. Test results of re-id algorithm on PC

重识别算法 mAP Rank1 Rank5 Rank10

DenseNet 0.643 0.847 0.943 0.963
OSNet 0.670 0.864 0.946 0.965

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