Single Object Tracking with Minimum False Positive using YOLOv4, VGG16, and Cosine Distance
DOI:
https://doi.org/10.30865/mib.v6i4.4827Keywords:
Single Object Tracking, Siamese Network, YOLOv4, VGG16, Cosine Distance, False PositiveAbstract
Siamese network is a solution to the problem of single object tracking. Siamese network is a comparison method that uses a neural network in it. In this case, the siamese network extracts several images with the same weight, then compares them. There have been many studies that use the siamese network for single object detection. However, many still have high false positives when the target object is in and out of the frame or is entirely blocked by something so that the target data stored has a high level of damage. This study aims to create a method to track an object (single object tracking), even though the object is blocked by something, temporarily exits the frame with minimum false positive to keep the target data clean. The authors developed the method based on YOLOv4, VGG16, and cosine distance. Furthermore, researchers combine these methods to solve these problems with the concept of a siamese network. The result is that the system can track a person even if the target is entirely blocked by something or even in and out of the frame and reappears in a different location with minimum false positive.References
Døble, Eirik & Haugseter, Sindre & Mikkelsen, Christian & Sneisen, Jørgen & Skeie, Nils-Olav & Brastein, Ole. (2022). Level Measurements with Computer vision - Comparison of traditional and modern Computer vision Methods. 140-147. 10.3384/ecp21185140.
Faraz Lotfi, & Hamid D. Taghirad. (2021). Single Object Tracking through a Fast and Effective Single-Multiple Model Convolutional Neural Network.
Cheewaprakobkit, Pimpa & Shih, Timothy & Lin, Chih-Yang & Liao, Hung-Chun. (2022). A Novel Relational Deep Network for Single Object Tracking. 102-107. 10.1109/KST53302.2022.9729070.
Zhongdao Wang, Liang Zheng, Yixuan Liu, Yali Li, & Shengjin Wang. (2020). Towards Real-Time Multi-Object Tracking.
Shuai, Bing & Berneshawi, Andrew & Li, Xinyu & Modolo, Davide & Tighe, Joseph. (2021). SiamMOT: Siamese Multi-Object Tracking. 12367-12377. 10.1109/CVPR46437.2021.01219.
Ilkin, Sumeyya & Gulagiz, Fidan & Akcakaya, Merve & Sahin, Suhap. (2022). Embedded Visual Object Tracking System Based on CSRT Tracker. 1-4. 10.1109/ICEIC54506.2022.9748840.
Teng, Fei & Liu, Qing & Zhu, Lin & Gao, Xueying. (2014). Robust multi-scale ship tracking via extended MIL tracker. 177-182. 10.2495/ICEEE20140211.
Xiaofeng, qiu & xiangrui, sun & yongchang, chen & Xinyan, Wang. (2021). Pedestrian detection and counting method based on YOLOv4+DeepSORT. 2. 10.1117/12.2618209.
Lev V. Utkin, Maxim S. Kovalev, & Ernest M. Kasimov. (2019). An explanation method for Siamese neural networks.
Paliwal, Pinak. (2021). Siamese Networks for Image Comparison and Discrepancy Localization: Siamese Networks in Exam Proctoring. 323-329. 10.1145/3488933.3488987.
Miao, Yilin & Liu, Zhewei & Wu, Xiangning & Gao, Jie. (2021). Cost-Sensitive Siamese Network for PCB Defect Classification. Computational Intelligence and Neuroscience. 2021. 1-13. 10.1155/2021/7550670.
Li, Da & Kang, Yabing & Xiang, Xing & Tao, WenSheng & Hu, Jiwei. (2022). Siamese Network with Feature Fusion for Visual Tracking. 1048-1052. 10.1109/ITOEC53115.2022.9734615.
Huang, Hanlin & Liu, Guixi & Zhang, Yi & Xiong, Ruke & Zhang, Shaoxuan. (2022). Ensemble siamese networks for object tracking. Neural Computing and Applications. 34. 1-19. 10.1007/s00521-022-06911-4.
Li, B., Yan, J., Wu, W., Zhu, Z., & Hu, X. (2018). High Performance Visual Tracking with Siamese Region Proposal Network. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8971-8980).
Zhou, Wei & Liu, Yuxiang & Xu, Haixia & Hu, Zhihai. (2022). A Modified SiamRPN for Visual Tracking. 10.1007/978-981-16-6963-7_70.
Pinky Sitikhu, Kritish Pahi, Pujan Thapa, & Subarna Shakya. (2019). A Comparison of Semantic Similarity Methods for Maximum Human Interpretability.
Rachevsky, Leonid & Kanevsky, Dimitri & Sarikaya, Ruhi & Ramabhadran, Bhuvana. (2011). Clustering with Modified Cosine Distance Learned from Constraints.. 1313-1316. 10.21437/Interspeech.2011-437.
Karen Simonyan, & Andrew Zisserman. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition.
Sheriff, S & Kumar J, Venkat & Vigneshwaran, S & Jones, Aida & Anand, Jose. (2021). Lung Cancer Detection using VGG NET 16 Architecture. Journal of Physics: Conference Series. 2040. 012001. 10.1088/1742-6596/2040/1/012001.
Cao, Ying & Gu, Runlong & Huang, Chenghua. (2022). Research on image recognition method based on LMAL and VGG-16. 98. 10.1117/12.2637770.
Taluja, Anuradha & Singhal, Jay & Gulati, Aastha & Gupta, Harshit. (2022). Gatekeeper Security Check System Using VGG. 10.1007/978-981-16-4016-2_66.
Yang, Zhen & Xu, Xuefei & Wang, Keke & Li, Xin & Ma, Chi. (2021). Multitarget Detection of Transmission Lines Based on DANet and YOLOv4. Scientific Programming. 2021. 1-12. 10.1155/2021/6235452.
Sathyamurthy, Kavi & Rajmohan, A.R. & Tejaswar, A. & Velayutham, Kavitha & Manimala, G.. (2021). Realtime Face Mask Detection Using TINY-YOLO V4. 169-174. 10.1109/ICCCT53315.2021.9711838.
Wani, L. & Momin, Md & Bhosale, Sharwari & Yadav, Abhishek & Nili, Manas. (2022). Vehicle Crash Detection using YOLO Algorithm. International Journal of Computer Science and Mobile Computing. 11. 75-82. 10.47760/ijcsmc.2022.v11i05.007.
Lisangan, Erick & Gormantara, Alfredo & Carolus, Ridnaldy. (2022). Implementasi Naive Bayes pada Analisis Sentimen Opini Masyarakat di Twitter Terhadap Kondisi New Normal di Indonesia. KONSTELASI: Konvergensi Teknologi dan Sistem Informasi. 2. 10.24002/konstelasi.v2i1.5609.
Romli, Ikhsan & Kharida, Fairuz & Naya, Chandra. (2020). Determination of Customer Satisfaction of Tax Service Office Services Using C4.5 and PSO. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). 4. 296-302. 10.29207/resti.v4i2.1718.
Wu, Yi & Lim, Jongwoo & Yang, Ming-Hsuan. (2015). Object Tracking Benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37. 1-1. 10.1109/TPAMI.2014.2388226.
Wang, Xiao & Shu, Xiujun & Zhang, Zhipeng & Jiang, Bo & Wang, Yaowei & Tian, Yonghong & Wu, Feng. (2021). Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark.
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