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Naval Joshi, Vaishnavi Bhutada, Manaswini Chittepu
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- Student, Student, Student, University of Petroleum and Energy studies, Bharati Vidyapeeth’s College of Engineering for Women, Pune, Amrita Vishwa Vidyapeetham, Uttarakhand, Maharashtra, Hyderabad, India, India, India
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Abstract
nThis 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
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Keywords: KCF, cyclist safety, urban environments, SLAM, RCNN
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Machine Systems and Manufacturing Technology(ijmsmt)]
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International Journal of Machine Systems and Manufacturing Technology
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| Volume | 01 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | March 14, 2024 | |
| Accepted | March 20, 2024 | |
| Published | May 9, 2024 |
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