![](http://storage.googleapis.com/journals-stmjournals-com-wp-media-to-gcp-offload/2022/10/vector.jpg)
Naval Joshi,
![](http://storage.googleapis.com/journals-stmjournals-com-wp-media-to-gcp-offload/2022/10/vector.jpg)
Vaishnavi Bhutada,
![](http://storage.googleapis.com/journals-stmjournals-com-wp-media-to-gcp-offload/2022/10/vector.jpg)
Manaswini Chittepu,
- Student University of Petroleum and Energy studies Uttarakhand India
- Student Bharati Vidyapeeth’s College of Engineering for Women, Pune Maharashtra India
- Student Amrita Vishwa Vidyapeetham Hyderabad India
Abstract
This study presents a multi-modal hazard detection system to enhance cyclist safety in urban environments. Lever- aging a combination of computer vision, object tracking, and predictive modeling, the system offers a comprehensive approach to identifying and mitigating potential risks. Key contributions include improved depth estimation through object size priors, multi-class tracking utilizing KCF and Brisk, and a novel recurrent neural network architecture for predicting bicycle movement. The system’s collision detection module evaluates the likelihood of collisions by analyzing future object and cyclist positions, incorporating uncertainties. While real-time implementation remains challenging due to hardware limitations, the modular design allows for future optimizations and integration with faster technologies. This research represents a valuable step toward safeguarding cyclists and paves the way for more effective hazard detection systems in the future
Keywords: KCF, cyclist safety, urban environments, SLAM, RCNN
[This article belongs to International Journal of Machine Systems and Manufacturing Technology(ijmsmt)]
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References
- F. Transport, “Table ras30001: Reported road casualties by road user type and severity, great britain, 2016,” Dft, Report, 2016. pages 1
- “Ras50001: Contributory factors in reported accidents by severity, great britain, 2016,” Dft, Report, 2016. pages 1
- “Focus on cycling in ‘reported road casualties great britain 2013,” Dft, Report, 2013. pages 1
- Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” vol. 25, 01 2012. pages 2
- Karpathy. (2017) Networks. [Online]. Available:http://cs231n. github.io/convolutional-networks/pages 2
- Li, H., Guo, X., Ouyang, B. D., & Wang, X. (2018). Neural network encapsulation. In Proceedings of the European conference on computer vision (ECCV) (pp. 252-267).
- Paik, I., Kwak, T., & Kim, I. (2019, October). Capsule networks need an improved routing algorithm. In Asian Conference on Machine Learning (pp. 489-502). PMLR.
- Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between capsules,” CoRR, vol. abs/1710.09829, 2017. [Online]. Available: http://arxiv.org/abs/1710.09829pages 2
- B. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” CoRR, vol. abs/1311.2524, 2013. [Online]. Available: http://arxiv.org/abs/1311.2524pages 2, 3
- Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” CoRR, vol. abs/1506.02640, 2015. [Online]. Available:http://arxiv.org/abs/1506. 02640pages 2, 23
- R. R. ”Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders, “”selective search for object recognition”,” ”International Journal of Computer Vision”, ”2013”. pages 3
- F. ”Felzenszwalb and D. P. Huttenlocher, “”efficient graph-based image segmentation”,” ”International Journal of Computer Vision”, ”2004”. pages 3
- B. Girshick, “Fast R-CNN,” CoRR, vol. abs/1504.08083, 2015. [Online]. Available:http://arxiv.org/abs/1504.08083pages 3, 4
- He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” CoRR, vol. abs/1406.4729, 2014. [Online]. Available:http://arxiv.org/abs/1406.4729 pages 3
- Ren, K. He, R. B. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” CoRR, vol. abs/1506.01497, 2015. [Online]. Available:http://arxiv.org/abs/1506. 01497pages 3, 5
- Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” CoRR, vol. abs/1411.4038, 2014. [Online]. Available: http://arxiv.org/abs/1411.4038pages 4, 5
- Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler,R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” CoRR, vol. abs/1604.01685, 2016. [Online]. Available:http://arxiv.org/abs/1604.01685pages 4
- Wan, M. Zeiler, S. Zhang, Y. LeCun, and R. Fergus, “Regularization of neural networks using dropconnect,” in Proceedings of the 30th International Conference on International Conference on Machine Learning – Volume 28, ser. ICML’13. JMLR.org, 2013, pp. III– 1058–III–1066. [Online]. Available:http://dl.acm.org/citation.cfm?id= 3042817.3043055pages 4
- Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” Journal of Machine Learning Research, vol. 15, pp. 1929–1958, 2014. [Online]. Available: http://jmlr.org/papers/v15/srivastava14a.htmlpages 5
- M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics). Berlin, Heidelberg: Springer-Verlag, 2006. pages 5
- He, G. Gkioxari, P. Doll a´r, and R. B. Girshick, “Mask R-CNN,” CoRR, vol. abs/1703.06870, 2017. [Online]. Available: http://arxiv.org/abs/1703.06870pages 5
- Olah, “Understanding lstms blog.” [Online]. Available:http://colah.github.io/posts/2015-08-Understanding-LSTMs/pages 5, 6
- Deisenroth, “Gaussian processes and bayesian optimisation,” 2018.pages 7
- Srinivas, A. Krause, S. Kakade, and M. Seeger, “Gaussian process optimization in the bandit setting: No regret and experimental design,” 2010. pages 6
- docs. (2012) Images. [Online]. Available:https://docs.opencv.org/2. 4/modules/calib3d/doc/camera calibration and 3d reconstruction.html pages 8
- Leutenegger, S. Lynen, M. Bosse, R. Siegwart, and P. Furgale, “Keyframe-based visual–inertial odometry using nonlinear optimiza- tion,” The International Journal of Robotics Research, vol. 34, no. 3, pp. 314–334, 2015. pages 9, 10, 11
- F. Durrant-Whyte, “Uncertain geometry in robotics,” vol. 4, pp. 23– 31, 03 1988. pages 10
- Bailey and H. Durrant-Whyte, “Simultaneous localization and map- ping (slam): Part i,” 10 2006. pages 10
- Leutenegger, M. Chli, and R. Y. Siegwart, “Brisk: Binary robust invariant scalable keypoints,” in 2011 International Conference on Computer Vision, Nov 2011, pp. 2548–2555. pages 10, 11
- docs. (2012) Images. [Online]. Available:http://opencv-python-tutroals.readthedocs.io/en/latest/py tutorials/ py feature2d/py features harris/py features harris.htmlpages 11
- F. Henriques, R. Caseiro, P. Martins, and J. Batista, “High-speed tracking with kernelized correlation filters,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 3, pp. 583–596,March 2015. pages 11, 12
- P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” CoRR, vol. abs/1412.6980, 2014. [Online]. Available: http://arxiv.org/abs/1412.6980pages 19
- Eisele. (2016) Circle intersection. [Online]. Available:https://www. xarg.org/2016/07/calculate-the-intersection-points-of-two-circles/pages 21
- (2016) Circle intersection. [Online]. Available:https://www.xarg. org/2016/07/calculate-the-intersection-area-of-two-circles/pages 21
Volume | 01 |
Issue | 02 |
Received | March 14, 2024 |
Accepted | March 20, 2024 |
Published | May 9, 2024 |