Vol. 3 No. 1 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

Deep Learning for Autonomous Vehicle Object Tracking and Recognition

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  • Dr. Xiaobo Li
Dr. Xiaobo Li
Associate Professor of Electrical Engineering, Tsinghua University, China
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Published 14-05-2023

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Xiaobo Li, “Deep Learning for Autonomous Vehicle Object Tracking and Recognition”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 1, pp. 375–403, May 2023, Accessed: Sep. 19, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/83

Abstract

Current tracking systems for autonomous vehicles are mostly reliant on object detection with detection-based tracking (DBT) (Li et al., 2016). However, to successfully track objects in an image series, these systems typically require an object detection step in the first frame, which could be challenging in cases of highly dynamic backgrounds or difficult object orientations. Additionally, they are susceptible to noise with sensor actors (e.g., pedestrians) after detection, failing to incorporate new target detections when detecting signals, and losing sight of targets (e.g., occlusions). The related task of visual multi-object tracking (V MOT) seeks to address these problems, yet it is also prone to significant issues, such as detection drifting, new object detections, as well as object occlusion and dynamic scenarios. As such, more robust algorithms that work well in different real-world dynamics and camera setups are required in the application of autonomous vehicles.[2]Autonomous vehicles rely on key perception capabilities including object detection, tracking and recognition to operate safely in the traffic ecosystem.

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References

  1. G. Zhang, J. Yin, P. Deng, Y. Sun et al., "Achieving Adaptive Visual Multi-Object Tracking with Unscented Kalman Filter," 2022. ncbi.nlm.nih.gov
  2. Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.
  3. Shahane, Vishal. "Harnessing Serverless Computing for Efficient and Scalable Big Data Analytics Workloads." Journal of Artificial Intelligence Research 1.1 (2021): 40-65.
  4. Abouelyazid, Mahmoud, and Chen Xiang. "Architectures for AI Integration in Next-Generation Cloud Infrastructure, Development, Security, and Management." International Journal of Information and Cybersecurity 3.1 (2019): 1-19.
  5. Prabhod, Kummaragunta Joel. "Utilizing Foundation Models and Reinforcement Learning for Intelligent Robotics: Enhancing Autonomous Task Performance in Dynamic Environments." Journal of Artificial Intelligence Research 2.2 (2022): 1-20.
  6. Tatineni, Sumanth, and Anirudh Mustyala. "AI-Powered Automation in DevOps for Intelligent Release Management: Techniques for Reducing Deployment Failures and Improving Software Quality." Advances in Deep Learning Techniques 1.1 (2021): 74-110.
  7. Y. Azadvatan and M. Kurt, "MelNet: A Real-Time Deep Learning Algorithm for Object Detection," 2024. [PDF]
  8. J. W. Pyo, S. H. Bae, S. H. Joo, M. K. Lee et al., "Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas," 2022. ncbi.nlm.nih.gov
  9. J. Zhang, Y. Liu, Q. Li, C. He et al., "Object Relocation Visual Tracking Based on Histogram Filter and Siamese Network in Intelligent Transportation," 2022. ncbi.nlm.nih.gov
  10. H. Wang, Y. Cai, and L. Chen, "A Vehicle Detection Algorithm Based on Deep Belief Network," 2014. ncbi.nlm.nih.gov
  11. W. Luo, B. Yang, and R. Urtasun, "Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net," 2020. [PDF]
  12. H. Bang, J. Min, and H. Jeon, "Deep Learning-Based Concrete Surface Damage Monitoring Method Using Structured Lights and Depth Camera," 2021. ncbi.nlm.nih.gov
  13. Y. Ed-Doughmi, N. Idrissi, and Y. Hbali, "Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network," 2020. ncbi.nlm.nih.gov
  14. W. Haider Bangyal, R. Qasim, N. ur Rehman, Z. Ahmad et al., "Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches," 2021. ncbi.nlm.nih.gov
  15. Z. Zhang, G. Li, Y. Xu, and X. Tang, "Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review," 2021. ncbi.nlm.nih.gov
  16. Y. Zhu, M. Wang, X. Yin, J. Zhang et al., "Deep Learning in Diverse Intelligent Sensor Based Systems," 2022. ncbi.nlm.nih.gov
  17. Z. Wei, F. Zhang, S. Chang, Y. Liu et al., "MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review," 2022. ncbi.nlm.nih.gov
  18. D. Fernandes, T. Afonso, P. Girão, D. Gonzalez et al., "Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup," 2021. ncbi.nlm.nih.gov
  19. C. Chen, B. Wang, C. Xiaoxuan Lu, N. Trigoni et al., "A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence," 2020. [PDF]
  20. R. Gandikota, "Computer Vision for Autonomous Vehicles," 2018. [PDF]
  21. A. Biglari and W. Tang, "A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme," 2023. ncbi.nlm.nih.gov
  22. H. Cao, W. Zou, Y. Wang, T. Song et al., "Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey," 2022. [PDF]
  23. Z. Wu, F. Li, Y. Zhu, K. Lu et al., "Design of a Robust System Architecture for Tracking Vehicle on Highway Based on Monocular Camera," 2022. ncbi.nlm.nih.gov
  24. A. Md Niamul Taufique, B. Minnehan, and A. Savakis, "Benchmarking Deep Trackers on Aerial Videos," 2021. [PDF]
  25. M. H. Sheu, S. M. Salahuddin Morsalin, J. X. Zheng, S. C. Hsia et al., "FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting," 2021. ncbi.nlm.nih.gov
  26. L. Arab Marcomini and A. Luiz Cunha, "Truck Axle Detection with Convolutional Neural Networks," 2022. [PDF]
  27. V. Thakar, W. Ahmed, M. M Soltani, and J. Yuan Yu, "Ensemble-based Adaptive Single-shot Multi-box Detector," 2018. [PDF]
  28. L. Liu, Z. Pan, and B. Lei, "Learning a Rotation Invariant Detector with Rotatable Bounding Box," 2017. [PDF]
  29. P. Srimuk, A. Boonpoonga, K. Kaemarungsi, K. Athikulwongse et al., "Implementation of and Experimentation with Ground-Penetrating Radar for Real-Time Automatic Detection of Buried Improvised Explosive Devices," 2022. ncbi.nlm.nih.gov
  30. N. Ghatwary, M. Zolgharni, and X. Ye, "Early esophageal adenocarcinoma detection using deep learning methods," 2019. ncbi.nlm.nih.gov
  31. N. Adiuku, N. P. Avdelidis, G. Tang, and A. Plastropoulos, "Advancements in Learning-Based Navigation Systems for Robotic Applications in MRO Hangar: Review," 2024. ncbi.nlm.nih.gov
  32. R. Bloomfield, G. Fletcher, H. Khlaaf, L. Hinde et al., "Safety Case Templates for Autonomous Systems," 2021. [PDF]
  33. M. Z. Alam, Z. Kaleem, and S. Kelouwani, "How to deal with glare for improved perception of Autonomous Vehicles," 2024. [PDF]
  34. Y. Zou, W. Zhang, W. Weng, and Z. Meng, "Multi-Vehicle Tracking via Real-Time Detection Probes and a Markov Decision Process Policy," 2019. ncbi.nlm.nih.gov
  35. L. Y. Lo, C. Hao Yiu, Y. Tang, A. S. Yang et al., "Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications," 2021. ncbi.nlm.nih.gov
  36. F. Leon and M. Gavrilescu, "A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving," 2019. [PDF]
  37. S. Scheidegger, J. Benjaminsson, E. Rosenberg, A. Krishnan et al., "Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering," 2018. [PDF]
  38. R. Caiand Peng Zhu, "Occlusion-aware Visual Tracker using Spatial Structural Information and Dominant Features," 2021. [PDF]
  39. L. Wang and D. Sng, "Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey," 2015. [PDF]
  40. S. M. Marshall, A. R. G. Murray, and L. Cronin, "A Probabilistic Framework for Quantifying Biological Complexity," 2017. [PDF]
  41. M. S. Bahraini, A. B. Rad, and M. Bozorg, "SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm," 2019. ncbi.nlm.nih.gov
  42. E. Khatab, A. Onsy, and A. Abouelfarag, "Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles," 2022. ncbi.nlm.nih.gov
  43. A. Luckow, M. Cook, N. Ashcraft, E. Weill et al., "Deep Learning in the Automotive Industry: Applications and Tools," 2017. [PDF]
  44. F. Shafiei Dizaji, "Lidar based Detection and Classification of Pedestrians and Vehicles Using Machine Learning Methods," 2019. [PDF]
  45. M. Córdova, A. Pinto, C. Carrozzo Hellevik, S. Abdel-Afou Alaliyat et al., "Litter Detection with Deep Learning: A Comparative Study," 2022. ncbi.nlm.nih.gov
  46. M. Carranza-García, P. Lara-Benítez, J. García-Gutiérrez, and J. C. Riquelme, "Enhancing Object Detection for Autonomous Driving by Optimizing Anchor Generation and Addressing Class Imbalance," 2021. [PDF]
  47. J. Sochor, J. Špaňhel, and A. Herout, "BoxCars: Improving Fine-Grained Recognition of Vehicles using 3-D Bounding Boxes in Traffic Surveillance," 2017. [PDF]
  48. K. Valev, A. Schumann, L. Sommer, and J. Beyerer, "A Systematic Evaluation of Recent Deep Learning Architectures for Fine-Grained Vehicle Classification," 2018. [PDF]
  49. J. Lian, Y. Yin, L. Li, Z. Wang et al., "Small Object Detection in Traffic Scenes Based on Attention Feature Fusion," 2021. ncbi.nlm.nih.gov
  50. Z. Wei, F. Zhang, S. Chang, Y. Liu et al., "MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review," 2021. [PDF]
  51. J. Janai, F. Güney, A. Behl, and A. Geiger, "Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art," 2017. [PDF]
  52. Y. Li and J. Ibanez-Guzman, "Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems," 2020. [PDF]
  53. J. Kaur and W. Singh, "Tools, techniques, datasets and application areas for object detection in an image: a review," 2022. ncbi.nlm.nih.gov
  54. D. Garikapati and S. Sudhir Shetiya, "Autonomous Vehicles: Evolution of Artificial Intelligence and Learning Algorithms," 2024. [PDF]
  55. S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, "A Survey of Deep Learning Techniques for Autonomous Driving," 2019. [PDF]
  56. J. Fayyad, M. A. Jaradat, D. Gruyer, and H. Najjaran, "Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review," 2020. ncbi.nlm.nih.gov