|
|
|
| Multi-target Tracking Algorithm Based on YOLOv3 Detection and Feature Point Matching |
| TAN Fang,MU Ping-an,MA Zhong-xue |
| School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China |
|
|
|
|
Abstract For multiple target tracking algorithm in the traditional pedestrian detection speed is slow, vulnerable to illumination change, the fast moving of pedestrians and the influence of partial occlusion cause the poor performance of pedestrian target tracking. According to the classic Tracking-by-Detection mode, a new pedestrian tracking algorithm is proposed, which uses deep learning YOLOv3 algorithm to detect pedestrian targets, and then uses fast corner detection algorithm and brisk feature point description algorithm to match the feature points of pedestrian targets between adjacent frames to achieve multi-target pedestrian tracking.The experimental results show that the pedestrian target achieves good continuous tracking effect under various complex environments of backlight, fast movement and partial occlusion, with an average accuracy of 87.7% and a speed of 35 frames per second.
|
|
Received: 18 June 2019
Published: 18 February 2021
|
|
|
|
Corresponding Authors:
Fang Tan
E-mail: 2608233411@qq.com
|
|
|
|
［1］何银飞. 基于改进的帧差法和背景差法实现运动目标检测［D］. 秦皇岛: 燕山大学, 2016.
［2］Brostow G J, Cipolla R. Unsupervised Bayesian Detection of Independent Motion in Crowds［C］// IEEE. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006.
［3］Castrillón-Santana M, Lorenzo-Navarro J, Hernández-Sosa D. People Semantic Description and Re-identification from Point Cloud Geometry［C］//IEEE Computer Society. International Conference on Pattern Recognition. 2014: 4702-4707.
［4］程淑红, 高许, 周 斌. 基于多特征提取和SVM参数优化的车型识别［J］. 计量学报, 2018, 39(3):348-352.
Cheng S H, Gao X, Zhou B. Vehicle identification based on multi-feature extraction and SVM parameter optimization［J］. Acta Metrologica Sinica, 2018, 39(3): 348-352.
［5］乔少杰, 韩楠, 朱新文, 等. 基于卡尔曼滤波的动态轨迹预测算法［J］. 电子学报, 2018, 46(2):418-423.
Qiao S J, Han N, Zhu X W, et al. Dynamic trajectory prediction algorithm based on kalman filter ［J］. Acta Electronica Sinica, 2018, 46(2): 418-423.
［6］陈思萌, 邓雨. 基于粒子滤波的视觉目标跟踪算法［J］. 计算机科学与应用, 2018, 8(5): 619-626.
Chen S M, Deng Y. Visual target tracking algorithm based on particle filter ［J］. Computer Science and Application, 2018, 8(5): 619-626.
［7］张立国, 李晓松, 肖磊, 等. 基于单目视觉的四旋翼飞行器目标跟踪算法研究［J］.计量学报, 2018, 39(3):342-347.
Zhang L G, Li X S, Xiao L, et al. Target tracking algorithm of quadrotor aircraft based on monocular vision ［J］. Acta Metrologica Sinica, 2018,39(3): 342-347.
［8］杨莉, 李茂林. 基于分块背景更新的漂移跟踪算法［J］. 计算机工程与设计, 2015, (6): 1571-1574,1613.
Yang L, Li M L. Drift tracking algorithm based on block background update［J］. Computer engineering and design, 2015, (6): 1571-1574,1613.
［9］Redmon J, Divvala S K, Girshick R, et al. You Only Look Once:Unified, Real-Time Object Detection［C］// IEEE Computer Society. Proceedings of Computer Vision and Pattern Recognition. Washington DC, 2016: 779-788.
［10］Redmon J,Farhadi A. YOLO9000: Better, Faster, Stronger［C］//IEEE Computer Society. Proceedings of Computer Vision and Pattern Recognition. Washington DC, 2017: 6517-6525.
［11］Redmon J, Farhadi A. YOLOv3:An Incremental Improvement［R］. Ithaca, NY: Cornell University, 2018.
［12］Liu W, Anguelov D, Erhan D, et al. SSD:Single Shot Multi Box Detector［C］//Proceedings of European Conference on Computer Vision.New York: Springer Press, 2015: 21-37.
［13］Khan F S, Anwer R M, Weijer J V D, et al. Deep Semantic Pyramids for Human Attributes and Action Recognition［C］//
IEEE Global Communications Conference, 2015.
［14］Ren S Q, He K M, Girshick R, et al. Faster R-CNN towards Real-Time Object Detection with Region Proposal Networks［C］//Proceedings of Annual Conference on Neural Information Processing Systems. Massachusetts: MIT Press, 2015: 91-99.
［15］He K, Gkioxari G, Dollár P, et al. Mask r-cnn［C］ //IEEE. 2017 IEEE International Conference on Computer Vision (ICCV). 2017: 2980-2988.
［16］Rosten E, Drummond T. Machine Learning for High Speed Corner Detection［C］// 9th European Conference on Computer Vision, 2006, 1: 430-443.
［17］Biadgie Y, Sohn K A. Feature Detector Using Adaptive Accelerated Segment Test［C］// International Conference on Information Science & Applications (ICISA). 2014: 1-4.
［18］Kashif M, Deserno T M, Haak D, et al. Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK? A General Question Answered for Bone Age Assessment［J］. Computers in Biology & Medicine, 2016, 68(C): 67-75.
［19］Aghajani K, Yousefpour R, Shirpour M, et al. Intensity based image registration by minimizing the complexity of weighted subtraction under illumination changes［J］. Biomedical Signal Processing and Control, 2016, 25: 35-45. |
|
|
|
2021, Vol. 42 
