A Re-ID and Tracking-by-detection Framework for Multiple Wildlife Tracking with Artiodactyla Characteristics in Ecological Surveillance

Long-term non-interventional observation of wild animals in the natural environment is very necessary for ecological protection. With the development of artificial intelligence, it is possible to effectively utilize the features of wild artiodactyls and realize multi-target tracking and reidentification. In this paper, a re-identification and tracking-by-detection framework is proposed for real-time tele-observation of Artiodactyla. According to the characteristics of artiodactyla, our algorithm designed a three-direction feature extraction and feature matching method to achieve re-identification. The kalman filter is used in cooperation with the detector to confirm the presence of the target. Our framework integrates detector, kalman tracker and KCF tracker, which alleviates the problem of discontinuous detection results caused by the low detection rate of artiodacods in the field environment. In the KCF tracking process, the tracking bounding boxes are corrected with high confidence detection results. The experiments demonstrate the feasibility and effectiveness of the framework.

[1]  Nikos Komodakis,et al.  Wide Residual Networks , 2016, BMVC.

[2]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Dietrich Paulus,et al.  Simple online and realtime tracking with a deep association metric , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[4]  Long Chen,et al.  Real-Time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification , 2018, 2018 IEEE International Conference on Multimedia and Expo (ICME).

[5]  Xin Zhao,et al.  Automatic multiple zebrafish tracking based on improved HOG features , 2018, Scientific Reports.

[6]  Volker Eiselein,et al.  High-Speed tracking-by-detection without using image information , 2017, 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[7]  Hilke Kieritz,et al.  Joint Detection and Online Multi-object Tracking , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[8]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Qi Tian,et al.  MARS: A Video Benchmark for Large-Scale Person Re-Identification , 2016, ECCV.

[10]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Ramakant Nevatia,et al.  Multi-target tracking by online learning of non-linear motion patterns and robust appearance models , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[13]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[14]  Longhui Wei,et al.  Person Transfer GAN to Bridge Domain Gap for Person Re-identification , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[15]  Ramakant Nevatia,et al.  An online learned CRF model for multi-target tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Konrad Schindler,et al.  Detection- and Trajectory-Level Exclusion in Multiple Object Tracking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Enkhbayar Erdenee,et al.  Multi-class Multi-object Tracking Using Changing Point Detection , 2016, ECCV Workshops.

[18]  Fabio Tozeto Ramos,et al.  Simple online and realtime tracking , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[19]  Hui Cheng,et al.  An autonomous vision-based target tracking system for rotorcraft unmanned aerial vehicles , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[20]  Jian Sun,et al.  AlignedReID: Surpassing Human-Level Performance in Person Re-Identification , 2017, ArXiv.