Abstract
Intelligent transportation systems (ITS) enable transportation participants to communicate with each other by sending and receiving messages, so that they can be aware of their surroundings and facilitate efficient transportation through better decision making. As an important part of ITS, autonomous vehicles can bring massive benefits by reducing traffic accidents. Correspondingly, much effort has been paid to the task of pedestrian detection, which is a fundamental task for supporting autonomous vehicles. With the progress of computational power in recent years, adopting deep learning–based methods has become a trend for improving the performance of pedestrian detection. In this article, we present design guidelines on deep learning–based pedestrian detection methods for supporting autonomous vehicles. First, we will introduce classic backbone models and frameworks, and we will analyze the inherent attributes of pedestrian detection. Then, we will illustrate and analyze representative pedestrian detectors from occlusion handling, multi-scale feature extraction, multi-perspective data utilization, and hard negatives handling these four aspects. Last, we will discuss the developments and trends in this area, followed by some open challenges.
- 2018. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. In SAE MOBILUS. Retrieved from https://www.sae.org/standards/content/j3016_201806/.Google Scholar
- 2020. 6 Key Connectivity Requirements of Autonomous Driving. In IEEE Spectrum. Retrieved from https://spectrum.ieee.org/transportation/advanced-cars/6-key-connectivity-requirements-of-autonomous-driving.Google Scholar
- 2020. New Level 3 Autonomous Vehicles Hitting the Road in 2020. In IEEE Innovation at Work. Retrieved from https://innovationatwork.ieee.org/new-level-3-autonomous-vehicles-hitting-the-road-in-2020/.Google Scholar
- T. Ahonen, A. Hadid, and M. Pietikainen. 2006. Face description with local binary patterns: Application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28, 12 (2006), 2037–2041.Google ScholarDigital Library
- E. Alemneh, S. Senouci, and P. Brunet. 2017. PV-Alert: A fog-based architecture for safeguarding vulnerable road users. In Proc. IEEE GIIS. 9–15.Google Scholar
- Anelia Angelova, Alex Krizhevsky, Vincent Vanhoucke, Abhijit Ogale, and Dave Ferguson. 2015. Real-time pedestrian detection with deep network cascades. In Proc. BMVC.Google ScholarCross Ref
- Felix Assion, Peter Schlicht, Florens Greßner, Wiebke Gunther, Fabian Huger, Nico Schmidt, and Umair Rasheed. 2019. The attack generator: A systematic approach towards constructing adversarial attacks. In Proc. IEEE CVPR Workshops. 1370–1379.Google ScholarCross Ref
- S. Baidya, Y. J. Ku, H. Zhao, J. Zhao, and S. Dey. 2020. Vehicular and edge computing for emerging connected and autonomous vehicle applications. In Proc. IEEE DAC. 1–6.Google Scholar
- Serge Belongie, Jitendra Malik, and Jan Puzicha. 2002. Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24, 4 (2002), 509–522.Google ScholarDigital Library
- Rodrigo Benenson, Markus Mathias, Radu Timofte, and Luc Van Gool. 2012. Pedestrian detection at 100 frames per second. In Proc. IEEE CVPR. 2903–2910.Google ScholarCross Ref
- Azzedine Boukerche, Burak Kantarci, and Cem Kaptan. 2020. Towards ensuring the reliability and dependability of vehicular crowd-sensing data in GPS-less location tracking. Pervas. Mob. Comput. 68 (2020), 101248.Google ScholarDigital Library
- M. Braun, S. Krebs, F. Flohr, and D. M. Gavrila. 2019. EuroCity persons: A novel benchmark for person detection in traffic scenes. IEEE Trans. Pattern Anal. Mach. Intell. 41, 8 (2019), 1844–1861.Google ScholarCross Ref
- Garrick Brazil, Xi Yin, and Xiaoming Liu. 2017. Illuminating pedestrians via simultaneous detection & segmentation. In Proc. IEEE ICCV. 4950–4959.Google ScholarCross Ref
- Zhaowei Cai, Quanfu Fan, Rogerio S. Feris, and Nuno Vasconcelos. 2016. A unified multi-scale deep convolutional neural network for fast object detection. In Proc. ECCV. 354–370.Google ScholarCross Ref
- Zhaowei Cai, Mohammad Saberian, and Nuno Vasconcelos. 2015. Learning complexity-aware cascades for deep pedestrian detection. In Proc. IEEE ICCV. 3361–3369.Google ScholarDigital Library
- Mingju Chen, Xiaofeng Han, Hua Zhang, Guojun Lin, and M. M. Kamruzzaman. 2019. Quality-guided key frames selection from video stream based on object detection. J. Vis. Commun. Image R. 65 (2019), 102678.Google ScholarCross Ref
- Shang-Tse Chen, Cory Cornelius, Jason Martin, and Duen Horng Polo Chau. 2018. Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector. In Proc. ECML PKDD. 52–68.Google Scholar
- Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. 2018. Model compression and acceleration for deep neural networks: The principles, progress, and challenges. IEEE Signal Process. Mag. 35, 1 (2018), 126–136.Google ScholarCross Ref
- Siew-Kei Lam, Chengju Zhou, and Meiqing Wu. 2019. SSA-CNN: Semantic Self-Attention CNN for Pedestrian Detection. [Online]. Retrieved from http://arxiv.org/abs/1902.09080.Google Scholar
- Cheng Chi, Shifeng Zhang, Junliang Xing, Zhen Lei, Stan Z. Li, and Xudong Zou. 2020. PedHunter: Occlusion robust pedestrian detector in crowded scenes. In Proc. AAAI. 10639–10646.Google ScholarCross Ref
- Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Mach. Learn. 20, 3 (1995), 273–297.Google ScholarCross Ref
- N. Dalal and B. Triggs. 2005. Histograms of oriented gradients for human detection. In Proc. IEEE CVPR. 886–893.Google Scholar
- Arthur Daniel Costea and Sergiu Nedevschi. 2016. Semantic channels for fast pedestrian detection. In Proc. IEEE CVPR. 2360–2368.Google ScholarCross Ref
- J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In Proc. IEEE CVPR. 248–255.Google ScholarCross Ref
- Piotr Dollár, Ron Appel, Serge Belongie, and Pietro Perona. 2014. Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36, 8 (2014), 1532–1545.Google ScholarDigital Library
- Piotr Dollár, Ron Appel, and Wolf Kienzle. 2012. Crosstalk cascades for frame-rate pedestrian detection. In Proc. ECCV. 645–659.Google ScholarCross Ref
- Piotr Dollar, Serge Belongie, and Pietro Perona. 2010. The fastest pedestrian detector in the west. In Proc. BMVC. 68.1–68.11.Google ScholarCross Ref
- Piotr Dollár, Zhuowen Tu, Pietro Perona, and Serge Belongie. 2009. Integral channel features. In Proc. BMVC. 244.1–244.11.Google ScholarCross Ref
- P. Dollar, C. Wojek, B. Schiele, and P. Perona. 2009. Pedestrian detection: A benchmark. In Proc. IEEE CVPR. 304–311.Google Scholar
- P. Dollar, C. Wojek, B. Schiele, and P. Perona. 2012. Pedestrian Detection: An evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34, 4 (2012), 743–761.Google ScholarDigital Library
- Fabio Henrique Kiyoiti dos Santos Tanaka and Claus Aranha. 2019. Data Augmentation Using GANs. [Online]. Retrieved from http://arxiv.org/abs/1904.09135.Google Scholar
- Kaiwen Duan, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang, and Qi Tian. 2019. Centernet: Keypoint triplets for object detection. In Proc. IEEE ICCV. 6569–6578.Google ScholarCross Ref
- A. Ess, B. Leibe, and L. Van Gool. 2007. Depth and appearance for mobile scene analysis. In Proc. IEEE ICCV. 1–8.Google Scholar
- Pedro Felzenszwalb, David McAllester, and Deva Ramanan. 2008. A discriminatively trained, multiscale, deformable part model. In Proc. IEEE CVPR. 1–8.Google ScholarCross Ref
- Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester, and Deva Ramanan. 2009. Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 9 (2009), 1627–1645.Google ScholarDigital Library
- Yoav Freund and Robert E. Schapire. 1997. A decision-theoretic generalization of on-line learning and an application to boosting. JCSS 55, 1 (1997), 119–139.Google ScholarDigital Library
- M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan. 2018. Synthetic data augmentation using GAN for improved liver lesion classification. In Proc. IEEE ISBI. 289–293.Google Scholar
- J. Funke, M. Brown, S. M. Erlien, and J. C. Gerdes. 2017. Collision Avoidance and stabilization for autonomous vehicles in emergency scenarios. IEEE Trans. Control Syst. Technol. 25, 4 (2017), 1204–1216.Google ScholarCross Ref
- A. Geiger, P. Lenz, and R. Urtasun. 2012. Are we ready for autonomous driving? The KITTI vision benchmark suite. In Proc. IEEE CVPR. 3354–3361.Google Scholar
- Ross Girshick. 2015. Fast R-CNN. In Proc. IEEE ICCV. 1440–1448.Google Scholar
- Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proc. IEEE CVPR. 580–587.Google ScholarDigital Library
- Marc Green. 2000. “How long does it take to stop?” Methodological Analysis of Driver Perception-Brake Times. Transp. Hum. Factors 2, 3 (2000), 195–216.Google ScholarCross Ref
- X. Guo, Y. Li, and H. Ling. 2017. LIME: Low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26, 2 (2017), 982–993.Google ScholarDigital Library
- Rajesh Gupta, Sudeep Tanwar, Neeraj Kumar, and Sudhanshu Tyagi. 2020. Blockchain-based security attack resilience schemes for autonomous vehicles in industry 4.0: A systematic review. Comput. Electr. Eng. 86 (2020), 106717.Google ScholarCross Ref
- Hangil Choi, S. Kim, Kihong Park, and K. Sohn. 2016. Multi-spectral pedestrian detection based on accumulated object proposal with fully convolutional networks. In Proc. IEEE ICPR. 621–626.Google Scholar
- Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik. 2014. Simultaneous detection and segmentation. In Proc. ECCV. 297–312.Google ScholarCross Ref
- Irtiza Hasan, Shengcai Liao, Jinpeng Li, Saad Ullah Akram, and Ling Shao. 2020. Pedestrian Detection: The Elephant In The Room. [Online]. Retrieved from https://arxiv.org/abs/2003.08799.Google Scholar
- A. Hbaieb, J. Rezgui, and L. Chaari. 2019. Pedestrian Detection for autonomous driving within cooperative communication system. In Proc. IEEE WCNC. 1–6.Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 9 (2015), 1904–1916.Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proc. IEEE CVPR. 770–778.Google ScholarCross Ref
- Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh. 2006. A fast learning algorithm for deep belief nets. Neural Comput. 18, 7 (2006), 1527–1554.Google ScholarDigital Library
- Mazin Hnewa and Hayder Radha. 2020. Rain-adaptive intensity-driven object detection for autonomous vehicles. In Proc. SAE WCX.Google ScholarCross Ref
- Jan Hosang, Mohamed Omran, Rodrigo Benenson, and Bernt Schiele. 2015. Taking a deeper look at pedestrians. In Proc. IEEE CVPR. 4073–4082.Google ScholarCross Ref
- Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. [Online]. Retrieved from http://arxiv.org/abs/1704.04861.Google Scholar
- Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-excitation networks. In Proc. IEEE CVPR. 7132–7141.Google ScholarCross Ref
- Shengyuan Hu, Tao Yu, Chuan Guo, Wei-Lun Chao, and Kilian Q. Weinberger. 2019. A new defense against adversarial images: Turning a weakness into a strength. In Proc. NIPS. 1635–1646.Google Scholar
- Xiaowei Hu, Chi-Wing Fu, Lei Zhu, and Pheng-Ann Heng. 2019. Depth-attentional features for single-image rain removal. In Proc. IEEE CVPR.Google ScholarCross Ref
- Qiangui Huang, Kevin Zhou, Suya You, and Ulrich Neumann. 2018. Learning to prune filters in convolutional neural networks. In Proc. IEEE WACV. 709–718.Google ScholarCross Ref
- Soonmin Hwang, Jaesik Park, Namil Kim, Yukyung Choi, and In So Kweon. 2015. Multispectral pedestrian detection: Benchmark dataset and baselines. In Proc. IEEE CVPR.Google ScholarCross Ref
- Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. Dally, and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. [Online]. Retrieved from http://arxiv.org/abs/1602.07360.Google Scholar
- Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proc. ICML. 448–456.Google Scholar
- Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. 2015. Spatial transformer networks. In Proc. NIPS. 2017–2025.Google Scholar
- Steve T. K. Jan, Joseph Messou, Yen-Chen Lin, Jia-Bin Huang, and Gang Wang. 2019. Connecting the digital and physical world: Improving the robustness of adversarial attacks. In Proc. AAAI, Vol. 33. 962–969.Google ScholarCross Ref
- I. Jegham and A. Ben Khalifa. 2017. Pedestrian detection in poor weather conditions using moving camera. In Proc. IEEE AICCSA. 358–362.Google Scholar
- Xiaoheng Jiang, Yanwei Pang, Xuelong Li, and Jing Pan. 2016. Speed up deep neural network based pedestrian detection by sharing features across multi-scale models. Neurocomputing 185 (2016), 163–170.Google ScholarDigital Library
- Shu Wang Jingjing Liu, Shaoting Zhang and Dimitris Metaxas. 2016. Multispectral deep neural networks for pedestrian detection. In Proc. BMVC. 73.1–73.13.Google Scholar
- S. Kato, S. Tokunaga, Y. Maruyama, S. Maeda, M. Hirabayashi, Y. Kitsukawa, A. Monrroy, T. Ando, Y. Fujii, and T. Azumi. 2018. Autoware on Board: Enabling autonomous vehicles with embedded systems. In Proc. IEEE ICCPS.Google Scholar
- Kaveh Bakhsh Kelarestaghi, Mahsa Foruhandeh, Kevin Heaslip, and Ryan Gerdes. 2019. Survey on vehicular ad hoc networks and its access technologies security vulnerabilities and countermeasures. [Online]. Retrieved from http://arxiv.org/abs/1903.01541.Google Scholar
- Jong Hyun Kim, Ganbayar Batchuluun, and Kang Ryoung Park. 2018. Pedestrian detection based on faster R-CNN in nighttime by fusing deep convolutional features of successive images. Expert Syst. Appl. 114 (2018), 15–33.Google ScholarCross Ref
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Proc. NIPS. 1097–1105.Google Scholar
- S. S. S. Kruthiventi, P. Sahay, and R. Biswal. 2017. Low-light pedestrian detection from RGB images using multi-modal knowledge distillation. In Proc. IEEE ICIP. 4207–4211.Google Scholar
- M. Kutila, P. Pyykonen, H. Holzhuter, M. Colomb, and P. Duthon. 2018. Automotive LiDAR performance verification in fog and rain. In Proc. IEEE ITSC. 1695–1701.Google Scholar
- Hei Law and Jia Deng. 2018. CornerNet: Detecting objects as paired keypoints. In Proc. ECCV. 734–750.Google ScholarCross Ref
- Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1998), 2278–2324.Google ScholarCross Ref
- J. Levinson, J. Askeland, J. Becker, J. Dolson, D. Held, S. Kammel, J. Z. Kolter, D. Langer, O. Pink, V. Pratt, M. Sokolsky, G. Stanek, D. Stavens, A. Teichman, M. Werling, and S. Thrun. 2011. Towards fully autonomous driving: Systems and algorithms. In IEEE IV. 163–168.Google Scholar
- Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin. 2019. Certified adversarial robustness with additive noise. In Proc. NIPS. 9464–9474.Google Scholar
- G. Li, Y. Yang, and X. Qu. 2019. Deep learning approaches on pedestrian detection in hazy weather. IEEE Trans. Ind. Electron. (2019), 1–1. Early Access.Google Scholar
- Jianan Li, Xiaodan Liang, ShengMei Shen, Tingfa Xu, Jiashi Feng, and Shuicheng Yan. 2017. Scale-aware fast R-CNN for pedestrian detection. IEEE Trans. Multimedia 20, 4 (2017), 985–996.Google Scholar
- Junwei Liang, Lu Jiang, and Alexander Hauptmann. 2020. SimAug: Learning Robust Representations from Simulation for Trajectory Prediction. [Online]. Retrieved from https://arxiv.org/abs/2004.02022.Google Scholar
- Chunze Lin, Jiwen Lu, Gang Wang, and Jie Zhou. 2018. Graininess-aware deep feature learning for pedestrian detection. In Proc. ECCV. 732–747.Google ScholarCross Ref
- Shih-Chieh Lin, Yunqi Zhang, Chang-Hong Hsu, Matt Skach, Md E. Haque, Lingjia Tang, and Jason Mars. 2018. The Architectural implications of autonomous driving: Constraints and acceleration. SIGPLAN Not. 53, 2 (2018), 751–766.Google ScholarDigital Library
- Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. 2017. Feature pyramid networks for object detection. In Proc. IEEE CVPR. 2117–2125.Google ScholarCross Ref
- Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. 2017. Focal loss for dense object detection. In Proc. IEEE ICCV. 2980–2988.Google ScholarCross Ref
- Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common objects in context. In Proc. ECCV. 740–755.Google Scholar
- Songtao Liu, Di Huang, and Yunhong Wang. 2019. Adaptive NMS: Refining pedestrian detection in a crowd. In Proc. IEEE CVPR. 6459–6468.Google ScholarCross Ref
- Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. 2016. SSD: Single shot multibox detector. In Proc. ECCV. 21–37.Google Scholar
- Wei Liu, Shengcai Liao, Weidong Hu, Xuezhi Liang, and Xiao Chen. 2018. Learning efficient single-stage pedestrian detectors by asymptotic localization fitting. In Proc. ECCV. 618–634.Google ScholarCross Ref
- Wei Liu, Shengcai Liao, Weiqiang Ren, Weidong Hu, and Yinan Yu. 2019. High-level semantic feature detection: A new perspective for pedestrian detection. In Proc. IEEE CVPR. 5187–5196.Google ScholarCross Ref
- Ping Luo, Yonglong Tian, Xiaogang Wang, and Xiaoou Tang. 2014. Switchable deep network for pedestrian detection. In Proc. IEEE CVPR. 899–906.Google ScholarDigital Library
- Abdelhamid Mammeri and Azzedine Boukerche. 2017. Inter-vehicle communication of warning information: an experimental study. Wireless Netw. 23, 6 (2017), 1837–1848.Google ScholarDigital Library
- Ignacio Martinez-Alpiste, Gelayol Golcarenarenji, Qi Wang, and Jose Maria Alcaraz-Calero. 2020. Real-time low-pixel infrared human detection from unmanned aerial vehicles. In Proc. DIVANet. 9–15.Google ScholarDigital Library
- S. K. Maurya and A. Choudhary. 2018. Deep learning–based vulnerable road user detection and collision avoidance. In Proc. IEEE ICVES. 1–6.Google Scholar
- Tsubasa Minematsu, Hideaki Uchiyama, Atsushi Shimada, Hajime Nagahara, and Rin-ichiro Taniguchi. 2017. Adaptive background model registration for moving cameras. Pattern Recogn. Lett. 96, C (2017), 86–95.Google Scholar
- Vinod Nair and Geoffrey E. Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In Proc. ICML. 807–814.Google Scholar
- Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han. 2015. Learning deconvolution network for semantic segmentation. In Proc. IEEE ICCV. 1520–1528.Google ScholarDigital Library
- Junhyug Noh, Soochan Lee, Beomsu Kim, and Gunhee Kim. 2018. Improving occlusion and hard negative handling for single-stage pedestrian detectors. In Proc. IEEE CVPR.Google ScholarCross Ref
- Junhyug Noh, Soochan Lee, Beomsu Kim, and Gunhee Kim. 2018. Improving occlusion and hard negative handling for single-stage pedestrian detectors. In Proc. IEEE CVPR. 966–974.Google ScholarCross Ref
- Nvidia. [n.d.]. NVIDIA DRIVE AGX PEGASUS. [Online]. Retrieved from https://www.nvidia.com/en-us/self-driving-cars/drive-platform/hardware/.Google Scholar
- Nvidia. [n.d.]. NVIDIA DRIVE SIM AND DRIVE CONSTELLATION. [Online]. Retrieved from https://www.nvidia.com/en-us/self-driving-cars/drive-constellation/.Google Scholar
- W. Ouyang and X. Wang. 2012. A discriminative deep model for pedestrian detection with occlusion handling. In Proc. IEEE CVPR. 3258–3265.Google Scholar
- Wanli Ouyang and Xiaogang Wang. 2013. Joint deep learning for pedestrian detection. In Proc. IEEE ICCV. 2056–2063.Google ScholarDigital Library
- Wanli Ouyang and Xiaogang Wang. 2013. Single-pedestrian detection aided by multi-pedestrian detection. In Proc. IEEE CVPR. 3198–3205.Google ScholarDigital Library
- Wanli Ouyang, Xingyu Zeng, and Xiaogang Wang. 2013. Modeling mutual visibility relationship in pedestrian detection. In Proc. IEEE CVPR. 3222–3229.Google ScholarDigital Library
- W. Ouyang, H. Zhou, H. Li, Q. Li, J. Yan, and X. Wang. 2018. Jointly learning deep features, deformable parts, occlusion and classification for pedestrian detection. IEEE Trans. Pattern Anal. Mach. Intell. 40, 8 (2018), 1874–1887.Google ScholarCross Ref
- Sakrapee Paisitkriangkrai, Chunhua Shen, and Anton Van Den Hengel. 2014. Strengthening the effectiveness of pedestrian detection with spatially pooled features. In Proc. ECCV. 546–561.Google ScholarCross Ref
- A. Palffy, J. F. P. Kooij, and D. M. Gavrila. 2019. Occlusion aware sensor fusion for early crossing pedestrian detection. In Proc. IEEE IV. 1768–1774.Google Scholar
- Yanwei Pang, Jin Xie, Muhammad Haris Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, and Ling Shao. 2019. Mask-guided attention network for occluded pedestrian detection. In Proc. IEEE ICCV. 4967–4975.Google ScholarCross Ref
- C. P. Papageorgiou, M. Oren, and T. Poggio. 1998. A general framework for object detection. In Proc. IEEE ICCV. 555–562.Google Scholar
- Constantine P. Papageorgiou, Michael Oren, and Tomaso Poggio. 1998. A general framework for object detection. In Proc. IEEE ICCV. 555–562.Google ScholarCross Ref
- Dennis Park, C. Lawrence Zitnick, Deva Ramanan, and Piotr Dollár. 2013. Exploring weak stabilization for motion feature extraction. In Proc. IEEE CVPR. 2882–2889.Google ScholarDigital Library
- Joel Pereira, Cristiano Premebida, Alireza Asvadi, F. Cannata, Luis Garrote, and U. J. Nunes. 2019. Test and evaluation of connected and autonomous vehicles in real-world scenarios. In Proc. IEEE IV. 14–19.Google Scholar
- J. Redmon and A. Farhadi. 2017. YOLO9000: Better, faster, stronger. In Proc. IEEE CVPR. 6517–6525.Google Scholar
- Kui Ren, Tianhang Zheng, Zhan Qin, and Xue Liu. 2020. Adversarial attacks and defenses in deep learning. Engineering 6, 3 (2020), 346–360.Google ScholarCross Ref
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. In Proc. NIPS. 91–99.Google Scholar
- Dominik Scherer, Andreas Müller, and Sven Behnke. 2010. Evaluation of pooling operations in convolutional architectures for object recognition. In Proc. ICANN. 92–101.Google ScholarCross Ref
- M. Sha and A. Boukerche. 2020. Semantic fusion-based pedestrian detection for supporting autonomous vehicles. In Proc. IEEE ISCC. 618–623.Google Scholar
- Weijing Shi, Mohamed Baker Alawieh, Xin Li, and Huafeng Yu. 2017. Algorithm and hardware implementation for visual perception system in autonomous vehicle: A survey. Integration 59 (2017), 148–156.Google ScholarDigital Library
- Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick. 2016. Training region-based object detectors with online hard example mining. In Proc. IEEE CVPR. 761–769.Google ScholarCross Ref
- Abdul Jabbar Siddiqui and Azzedine Boukerche. 2020. Adversarial patches-based attacks on automated vehicle make and model recognition systems. In Proc. Q2SWinet. 115–121.Google ScholarDigital Library
- Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. [Online]. Retrieved from http://arxiv.org/abs/1409.1556. In Proc. ICLR.Google Scholar
- Tao Song, Leiyu Sun, Di Xie, Haiming Sun, and Shiliang Pu. 2018. Small-scale pedestrian detection based on topological line localization and temporal feature aggregation. In Proc. ECCV. 536–551.Google ScholarCross Ref
- Jack Stewart. [n.d.]. Self-Driving Cars Use Crazy Amounts of Power, and It’s Becoming a Problem. [Online]. Retrieved from https://www.wired.com/story/self-driving-cars-power-consumption-nvidia-chip/.Google Scholar
- Peng Sun, Noura AlJeri, and Azzedine Boukerche. 2020. An energy-efficient proactive handover scheme for vehicular networks based on passive RSU detection. IEEE Trans. Sustain. Comput. 5, 1 (2020), 37–47.Google ScholarCross Ref
- Siyu Tang, Mykhaylo Andriluka, and Bernt Schiele. 2014. Detection and tracking of occluded people. Int. J. Comput. Vis. 110, 1 (2014), 58–69.Google ScholarCross Ref
- Yonglong Tian, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015. Deep learning strong parts for pedestrian detection. In Proc. IEEE ICCV. 1904–1912.Google ScholarDigital Library
- Yonglong Tian, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015. Pedestrian detection aided by deep learning semantic tasks. In Proc. IEEE CVPR. 5079–5087.Google ScholarCross Ref
- Cosmin Toca, Mihai Ciuc, and Carmen Patrascu. 2015. Normalized autobinomial Markov channels for pedestrian detection. In Proc. BMVC. 175.1–175.13.Google Scholar
- Alexander Toshev and Christian Szegedy. 2014. DeepPose: Human pose estimation via deep neural networks. In Proc. IEEE CVPR.Google ScholarDigital Library
- Jasper R. R. Uijlings, Koen E. A. Van De Sande, Theo Gevers, and Arnold W. M. Smeulders. 2013. Selective search for object recognition. Int. J. Comput. Vision 104, 2 (2013), 154–171.Google ScholarDigital Library
- Jessica Van Brummelen, Marie O’Brien, Dominique Gruyer, and Homayoun Najjaran. 2018. Autonomous vehicle perception: The technology of today and tomorrow. Trans. Res. Part C Emerg. Technol. 89 (2018), 384–406.Google ScholarCross Ref
- Jaycil Z. Varghese, Randy G. Boone, et al. 2015. Overview of autonomous vehicle sensors and systems. In Proc. IEOM. 178–191.Google Scholar
- Paul Viola and Michael J. Jones. 2004. Robust real-time face detection. Int. J. Comput. Vis 57, 2 (2004), 137–154.Google ScholarDigital Library
- Tuan-Hung Vu, Anton Osokin, and Ivan Laptev. 2015. Context-aware CNNs for person head detection. In Proc. IEEE ICCV. 2893–2901.Google ScholarDigital Library
- Heng Wang, Bin Wang, Bingbing Liu, Xiaoli Meng, and Guanghong Yang. 2017. Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle. Rob. Auton. Syst. 88 (2017), 71–78.Google ScholarDigital Library
- Shiguang Wang, Jian Cheng, Haijun Liu, Feng Wang, and Hui Zhou. 2018. Pedestrian detection via body part semantic and contextual information with DNN. IEEE Trans. Multimedia 20, 11 (2018), 3148–3159.Google ScholarDigital Library
- Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson W. H. Lau. 2019. Spatial attentive single-image deraining with a high quality real rain dataset. In Proc. IEEE CVPR.Google Scholar
- Xiaoyu Wang, Tony X. Han, and Shuicheng Yan. 2009. An HOG-LBP human detector with partial occlusion handling. In Proc. IEEE ICCV. 32–39.Google ScholarCross Ref
- Xiaolong Wang, Abhinav Shrivastava, and Abhinav Gupta. 2017. A-fast-rcnn: Hard positive generation via adversary for object detection. In Proc. IEEE CVPR. 2606–2615.Google ScholarCross Ref
- Xinlong Wang, Tete Xiao, Yuning Jiang, Shuai Shao, Jian Sun, and Chunhua Shen. 2018. Repulsion loss: Detecting pedestrians in a crowd. In Proc. IEEE CVPR. 7774–7783.Google ScholarCross Ref
- Christian Wojek and Bernt Schiele. 2008. A performance evaluation of single and multi-feature people detection. In Proc. JPRS. 82–91.Google ScholarDigital Library
- Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. 2018. Cbam: Convolutional block attention module. In Proc. ECCV. 3–19.Google ScholarCross Ref
- Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, and Alan Yuille. 2017. Adversarial examples for semantic segmentation and object detection. In Proc. IEEE ICCV. 1369–1378.Google ScholarCross Ref
- H. Xiong, F. B. Flohr, S. Wang, B. Wang, J. Wang, and K. Li. 2019. Recurrent neural network architectures for vulnerable road user trajectory prediction. In Proc. IEEE IV. 171–178.Google Scholar
- Bin Yang, Junjie Yan, Zhen Lei, and Stan Z. Li. 2015. Convolutional channel features. In Proc. IEEE ICCV. 82–90.Google Scholar
- S. Zang, M. Ding, D. Smith, P. Tyler, T. Rakotoarivelo, and M. A. Kaafar. 2019. The impact of adverse weather conditions on autonomous vehicles: How rain, snow, fog, and hail affect the performance of a self-driving car. IEEE Veh. Technol. Mag. 14, 2 (2019), 103–111.Google ScholarCross Ref
- He Zhang and Vishal M. Patel. 2018. Densely connected pyramid dehazing network. In Proc. IEEE CVPR.Google Scholar
- Jialiang Zhang, Lixiang Lin, Yun-chen Chen, Yao Hu, Steven C. H. Hoi, and Jianke Zhu. 2019. CSID: Center, Scale, Identity and Density-aware Pedestrian Detectionin a Crowd. [Online]. Retrieved from https://arxiv.org/abs/1910.09188.Google Scholar
- Liliang Zhang, Liang Lin, Xiaodan Liang, and Kaiming He. 2016. Is faster r-cnn doing well for pedestrian detection? In Proc. ECCV. 443–457.Google ScholarCross Ref
- Shanshan Zhang, Christian Bauckhage, and Armin B. Cremers. 2014. Informed haar-like features improve pedestrian detection. In Proc. IEEE CVPR.Google Scholar
- Shanshan Zhang, Rodrigo Benenson, Mohamed Omran, Jan Hosang, and Bernt Schiele. 2016. How far are we from solving pedestrian detection? In Proc. IEEE CVPR. 1259–1267.Google ScholarCross Ref
- S. Zhang, R. Benenson, and B. Schiele. 2017. CityPersons: A diverse dataset for pedestrian detection. In Proc. IEEE CVPR. 4457–4465.Google Scholar
- Shanshan Zhang, Rodrigo Benenson, Bernt Schiele, et al. 2015. Filtered channel features for pedestrian detection. In Proc. IEEE CVPR. 1751–1760.Google ScholarCross Ref
- Shifeng Zhang, Longyin Wen, Xiao Bian, Zhen Lei, and Stan Z. Li. 2018. Occlusion-aware R-CNN: Detecting pedestrians in a crowd. In Proc. ECCV. 637–653.Google Scholar
- Shanshan Zhang, Jian Yang, and Bernt Schiele. 2018. Occluded pedestrian detection through guided attention in CNNs. In Proc. IEEE CVPR. 6995–7003.Google ScholarCross Ref
- Xiaowei Zhang, Li Cheng, Bo Li, and Hai-Miao Hu. 2018. Too far to see? Not really!–pedestrian detection with scale-aware localization policy. IEEE Trans. Image Process. 27, 8 (2018), 3703–3715.Google ScholarCross Ref
- Zhen Jia, A. Balasuriya, and S. Challa. 2006. Recent developments in vision based target tracking for autonomous vehicles navigation. In Proc. IEEE Trans. Intell. Transp. Syst.765–770.Google Scholar
- Chunluan Zhou and Junsong Yuan. 2017. Multi-label learning of part detectors for heavily occluded pedestrian detection. In Proc. IEEE ICCV. 3486–3495.Google ScholarCross Ref
- Chunluan Zhou and Junsong Yuan. 2018. Bi-box regression for pedestrian detection and occlusion estimation. In Proc. ECCV. 135–151.Google ScholarCross Ref
- Yousong Zhu, Jinqiao Wang, Chaoyang Zhao, Haiyun Guo, and Hanqing Lu. 2016. Scale-adaptive deconvolutional regression network for pedestrian detection. In Proc. ACCV. 416–430.Google Scholar
Index Terms
- Design Guidelines on Deep Learning–based Pedestrian Detection Methods for Supporting Autonomous Vehicles
Recommendations
Dynamic intersections and self-driving vehicles
ICCPS '18: Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical SystemsConnected and automated vehicles are expected to be at the core of future intelligent transportation systems. One of the main practical challenges for self-driving vehicles on public roads is safe cooperation and collaboration among multiple vehicles ...
A novel visibility semantic feature-aided pedestrian detection scheme for autonomous vehicles
AbstractIntelligent transportation systems (ITS) have become a popular method for enhancing transportation safety and efficiency. As essential participants of ITS, autonomous vehicles need to detect pedestrians accurately. In this paper, we ...
Performance evaluation of CNN-based pedestrian detectors for autonomous vehicles
AbstractWith the widespread application of deep learning methodologies, many fields including Intelligent Transportation Systems (ITS) have integrated neural network-based models. In return, the promising performance of neural network-based ...
Comments