Computer Science ›› 2023, Vol. 50 ›› Issue (1): 131-137.doi: 10.11896/jsjkx.211100097

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Multi-object Tracking Based on Cross-correlation Attention and Chained Frames

CHEN Yunfang, LU Yangyang, ZHOU Xin, ZHANG Wei   

  1. School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
  • Received:2021-11-08 Revised:2022-06-30 Online:2023-01-15 Published:2023-01-09
  • About author:CHEN Yunfang,born in 1976,Ph.D,master supervisor.His main research interests include artificial intelligence algorithms,functional analysis of specific application areas,application deve-lopment using intelligent systems.
    ZHANG Wei,born in 1973,Ph.D,Ph.D supervisor.His main research interests include intelligent perception and cognition under UAV platform,privacy protection and artificial intelligence security.
  • Supported by:
    National Key R & D Program of China(2019YFB2101700).

Abstract: The one-stage method of multi-object tracking(MOT) has gradually become the mainstream of MOT due to its advantages in reasoning speed.However,compared with the two-stage method,its tracking accuracy is poor.One reason is that the target is easy to be lost due to the use of single frame input that cause the correlation between the targets is not strong,the other is that the difference between the two tasks of detection and tracking is ignored.In order to alleviate the limitations,a multi-object tracking algorithm based on cross-correlation attention and chained frames(MOT-CCC) is proposed.MOT-CCC takes two consecutive frames as input,and converts the target association problem into a two-frame detection frame pair regression problem,which enhances the correlation between targets.The cross-correlation attention module decouples the detection task and the identification task to balance and reduce the competition between the two tasks.In addition,the proposed algorithm integrates the three modules of target detection,feature extraction and data association into one whole network to achieve end-to-end optimization,which improves tracking accuracy and reduces tracking time.In the MOT16 and MOT17 benchmark tests,compared with the benchmark CTracker algorithm,the MOTA of MOT-CCC increases by 1.3% and the FP decreases by 13%.

Key words: Multi-object tracking, Chained tracker, Cross-correlation attention, One-shot, End-to-End

CLC Number: 

  • TP391
[1]LEE B,ERDENEE E,JIN S,et al.Multi-class multi-objecttracking using changing point detection[C]//European Confe-rence on Computer Vision.Cham:Springer,2016:68-83.
[2]WOJKE N,BEWLEY A,PAULUS D.Simple online and real-time tracking with a deep association metric[C]//2017 IEEE International Conference on Image Processing(ICIP).IEEE,2017:3645-3649.
[3]FANG K,XIANG Y,LI X,et al.Recurrent autoregressive networks for online multi-object tracking[C]//2018 IEEE Winter Conference on Applications of Computer Vision(WACV).IEEE,2018:466-475.
[4]FARHADI A,REDMON J.Yolov3:An incremental improve-ment[C]//Computer Vision and Pattern Recognition.Berlin/Heidelberg,Germany:Springer,2018:1804-02.
[5]REN S,HE K,GIRSHICK R,et al.Faster r-cnn:Towards real-time object detection with region proposal networks[J].Advances in Neural Information Processing Systems,2015,28:91-99.
[6]GONG X,LE Z C,WNAG H,et al.Survey of Data Association Technology in Multi-target Tracking[J].Computer Science,2020,47(10):136-144.
[7]SADEGHIAN A,ALAHI A,SAVARESE S.Tracking the untrackable:Learning to track multiple cues with long-term dependencies[C]//Proceedings of the IEEE International Confe-rence on Computer Vision.2017:300-311.
[8]REZATOFIGHI S H,MILAN A,ZHANG Z,et al.Joint probabilistic data association revisited[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:3047-3055.
[9]KIM C,LI F,CIPTADI A,et al.Multiple hypothesis trackingrevisited[C]//Proceedings of the IEEE International Confe-rence on Computer Vision.2015:4696-4704.
[10]LEAL-TAIXÉ L,CANTON-FERRER C,SCHINDLER K.Learning by tracking:Siamese CNN for robust target association[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2016:33-40.
[11]SUN S J,AKHTAR N,SONG H S,et al.Deep affinity network for multiple object tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,43(1):104-119.
[12]MAHMOUDI N,AHADI S M,RAHMATI M.Multi-targettracking using CNN-based features:CNNMTT[J].Multimedia Tools and Applications,2019,78(6):7077-7096.
[13]BAE S H,YOON K J.Confidence-based data association and discriminative deep appearance learning for robust online multi-object tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,40(3):595-610.
[14]BERGMANN P,MEINHARDT T,LEAL-TAIXE L.Tracking without bells and whistles[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:941-951.
[15]WANG Z,ZHENG L,LIU Y,et al.Towards real-time multi-object tracking[C]//Computer Vision-ECCV 2020:16th European Conference,Glasgow,UK(Part XI 16).Springer International Publishing,2020:107-122.
[16]LIN T Y,DOLLÁR P,GIRSHICK R,et al.Feature pyramidnetworks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2117-2125.
[17]ZHANG Y,WANG C,WANG X,et al.A simple baseline for multi-object tracking[J].arXiv:2004.01888,2020.
[18]CHEN L,AI H,ZHUANG Z,et al.Real-time multiple people tracking with deeply learned candidate selection and person re-identification[C]//2018 IEEE International Conference on Multimedia and Expo(ICME).IEEE,2018:1-6.
[19]KUHN H W.The Hungarian method for the assignment problem[J].Naval Research Logistics Quarterly,1955,2(1/2):83-97.
[20]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[21]LIU W,ANGUELOV D,ERHAN D,et al.Ssd:Single shot multibox detector[C]//European Conference on Computer vision.Cham:Springer,2016:21-37.
[22]FELZENSZWALB P F,GIRSHICK R B,MCALLESTER D,et al.Object detection with discriminatively trained part-based models[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,32(9):1627-1645.
[23]BERNARDIN K,STIEFELHAGEN R.Evaluating multiple object tracking performance:the clear mot metrics[J].EURASIP Journal on Image and Video Processing,2008,2008:1-10.
[24]HE K,ZHANG X,REN S,et al.Delving deep into rectifiers:Surpassing human-level performance on imagenet classification[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:1026-1034.
[25]YU F,LI W,LI Q,et al.Poi:Multiple object tracking with high performance detection and appearance feature[C]//European Conference on Computer Vision.Cham:Springer,2016:36-42.
[26]PENG J,WANG C,WAN F,et al.Chained-tracker:Chainingpaired attentive regression results for end-to-end joint multiple-object detection and tracking[C]//European Conference on Computer Vision.Cham:Springer,2020:145-161.
[27]KIM C,LI F,REHG J M.Multi-object tracking with neural gating using bilinear lstm[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:200-215.
[28]CHEN J,SHENG H,ZHANG Y,et al.Enhancing detectionmodel for multiple hypothesis tracking[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2017:18-27.
[29]ZHU J,YANG H,LIU N,et al.Online multi-object tracking with dual matching attention networks[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:366-382.
[30]CHOI W.Near-online multi-target tracking with aggregated local flow descriptor[C]//Proceedings of the IEEE International Conference on Computer Vision.2015:3029-3037.
[31]KEUPER M,TANG S,ANDRES B,et al.Motion segmentation &multiple object tracking by correlation co-clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,42(1):140-153.
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