A Novel Efficient Dual-Gate Mixed Dilated Convolution Network for Multi-Scale Pedestrian Detection
Received: 29 August 2023 | Revised: 13 September 2023 | Accepted: 16 September 2023 | Online: 5 December 2023
Corresponding author: Etikala Raja Vikram Reddy
Abstract
With the increasing use of onboard high-speed computing systems, vehicle manufacturers are offering significant advanced features of driver assistance systems. Pedestrian detection is one of the major requirements of such systems, which commonly use cameras, radar, and ultrasonic sensors. Image recognition based on captured image streams is one of the powerful tools used for the detection of pedestrians, which exhibits similarities and distinguishing features compared to general object detection. Although pedestrian detection has advanced significantly along with deep learning, some issues still need to be addressed. Pedestrian detection is essential for several real-world applications and is an initial step in outdoor scene analysis. Typically, in a crowded situation, conventional detectors are unable to distinguish persons from each other successfully. This study presents a novel technique, based on the Dual Gate Mixed Dilated Convolution Network, to address this problem by adaptively filtering spatial areas where the patterns are still complicated and require further processing. The proposed technique manages obscured patterns while offering improved multiscale pedestrian recognition accuracy.
Keywords:
deep learning, image recognition, mixed dilated convolution, multiscale pedestrian recognition, spatial regionsDownloads
References
X. Wang, T. Xiao, Y. Jiang, S. Shao, J. Sun, and C. Shen, "Repulsion Loss: Detecting Pedestrians in a Crowd," presented at the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, Jun. 2018, pp. 7774–7783.
G. Brazil, X. Yin, and X. Liu, "Illuminating Pedestrians via Simultaneous Detection and Segmentation," in 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, Jul. 2017, pp. 4960–4969.
J. Noh, S. Lee, B. Kim, and G. Kim, "Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, Jun. 2018, pp. 966–974.
W. Liu, S. Liao, W. Hu, X. Liang, and X. Chen, "Learning Efficient Single-Stage Pedestrian Detectors by Asymptotic Localization Fitting," in Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part XIV, Munich, Germany, Jun. 2018, pp. 643–659.
J. Mao, T. Xiao, Y. Jiang, and Z. Cao, "What Can Help Pedestrian Detection?," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, pp. 6034–6043.
J. Mao, T. Xiao, Y. Jiang, and Z. Cao, "What Can Help Pedestrian Detection?," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Venice, Italy, Jul. 2017, pp. 6034–6043.
Y. Tian, P. Luo, X. Wang, and X. Tang, "Deep Learning Strong Parts for Pedestrian Detection," in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, Sep. 2015, pp. 1904–1912.
S. Zhang, J. Yang, and B. Schiele, "Occluded Pedestrian Detection Through Guided Attention in CNNs," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, Jun. 2018, pp. 6995–7003.
S. Zhang, L. Wen, X. Bian, Z. Lei, and S. Z. Li, "Occlusion-Aware R-CNN: Detecting Pedestrians in a Crowd," in Computer Vision – ECCV 2018, Munich, Germany, 2018, pp. 657–674.
S. Wang, J. Cheng, H. Liu, and M. Tang, "PCN: Part and Context Information for Pedestrian Detection with CNNs." arXiv, Apr. 12, 2018.
D. Patil and S. Jadhav, "Road Segmentation in High-Resolution Images Using Deep Residual Networks," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9654–9660, Dec. 2022.
S. Rani, Y. Chabrra, and K. Malik, "An Improved Denoising Algorithm for Removing Noise in Color Images," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8738–8744, Jun. 2022.
D. K. Suker, "Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy," Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8393–8401, Apr. 2022.
C. Lin, J. Lu, G. Wang, and J. Zhou, "Graininess-Aware Deep Feature Learning for Robust Pedestrian Detection," IEEE Transactions on Image Processing, vol. 29, pp. 3820–3834, 2020.
W. Liu, S. Liao, W. Ren, W. Hu, and Y. Yu, "High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection," in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, Jun. 2019, pp. 5182–5191.
T. Takikawa, D. Acuna, V. Jampani, and S. Fidler, "Gated-SCNN: Gated Shape CNNs for Semantic Segmentation," in 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), Jul. 2019, pp. 5228–5237.
Y. Luo, C. Zhang, M. Zhao, H. Zhou, and J. Sun, "Where, What, Whether: Multi-Modal Learning Meets Pedestrian Detection," presented at the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 14065–14073.
C. Guo, B. Fan, Q. Zhang, S. Xiang, and C. Pan, "AugFPN: Improving Multi-Scale Feature Learning for Object Detection," in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, Jun. 2020, pp. 12592–12601.
P. Yang, G. Zhang, L. Wang, L. Xu, Q. Deng, and M.-H. Yang, "A Part-Aware Multi-Scale Fully Convolutional Network for Pedestrian Detection," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 2, pp. 1125–1137, Oct. 2021.
T. Liu, J. J. Huang, T. Dai, G. Ren, and T. Stathaki, "Gated Multi-Layer Convolutional Feature Extraction Network for Robust Pedestrian Detection," in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, Feb. 2020, pp. 3867–3871.
Y. Tan, H. Yao, H. Li, X. Lu, and H. Xie, "PRF-Ped: Multi-scale Pedestrian Detector with Prior-based Receptive Field," in 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, Jan. 2021, pp. 6059–6064.
M. Liu, C. Zhu, J. Wang, and X. C. Yin, "Adaptive Pattern-Parameter Matching for Robust Pedestrian Detection," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 3, pp. 2154–2162, May 2021.
Q. Geng, H. Zhang, X. Qi, G. Huang, R. Yang, and Z. Zhou, "Gated Path Selection Network for Semantic Segmentation," IEEE Transactions on Image Processing, vol. 30, pp. 2436–2449, 2021.
X. Li, Z. Liu, P. Luo, C. C. Loy, and X. Tang, "Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 6459–6468.
C. J. Maddison, A. Mnih, and Y. W. Teh, "The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables." arXiv, Mar. 05, 2017.
J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, "Squeeze-and-Excitation Networks," IEEE transactions on pattern analysis and machine intelligence, vol. 42, no. 8, pp. 2011–2023, Aug. 2020.
T. Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature Pyramid Networks for Object Detection," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 936–944.
F. Yu and V. Koltun, "Multi-Scale Context Aggregation by Dilated Convolutions." arXiv, Apr. 30, 2016.
P. Wang et al., "Understanding Convolution for Semantic Segmentation," in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, Mar. 2018, pp. 1451–1460.
S. Zhang, R. Benenson, and B. Schiele, "CityPersons: A Diverse Dataset for Pedestrian Detection," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 4457–4465.
J. Zhang et al., "Attribute-Aware Pedestrian Detection in a Crowd," IEEE Transactions on Multimedia, vol. 23, pp. 3085–3097, 2021.
I. Hasan, S. Liao, J. Li, S. U. Akram, and L. Shao, "Generalizable Pedestrian Detection: The Elephant In The Room," in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2021, pp. 11323–11332.
L. Shi, C. Livermore, and I. A. Kakadiaris, "DVRNet: Decoupled Visible Region Network for Pedestrian Detection," in 2020 IEEE International Joint Conference on Biometrics (IJCB), Houston, TX, USA, Sep. 2020, pp. 1–9.
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