International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024
p-ISSN: 2395-0072
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SAFE ROAD AI: REAL-TIME ACCIDENT DETECTION FROM MULTI-ANGLE CRASH VIDEOS: A REVIEW Piyush Kumar1, Dipti Ranjan Tiwari2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Road accidents represent a prominent cause of
fatalities on a global scale, underscoring the need for advanced technologies to promptly detect and respond to such incidents. This comprehensive review delves into the evolution and implementation of Safe Road AI, a cutting-edge real-time accident detection system that harnesses multi-angle crash videos. Through meticulous analysis of footage from diverse viewpoints, Safe Road AI utilizes state-of-the-art computer vision techniques and deep learning algorithms to precisely identify and categorize traffic accidents as they unfold. The system's capacity to process intricate visual data instantaneously confers a notable advantage in diminishing emergency response times and enhancing overall road safety. This paper conducts a thorough evaluation of existing methodologies in accident detection, shedding light on the obstacles and breakthroughs in multi-angle video analysis. Furthermore, it delves into the ramifications of Safe Road AI in fortifying road safety measures, influencing policy-making endeavors, and delineating future research paths within the realm of intelligent transportation systems. Key Words: Safe Road AI, real-time accident detection, multi-angle crash videos, computer vision, deep learning, traffic safety, intelligent transportation systems, emergency response, road safety technology.
1.BACKGROUND "Safe Road AI" is an innovative technology crafted to heighten road safety by employing real-time accident detection through the scrutiny of multi-angle crash videos. The genesis of this technology can be attributed to the escalating global apprehension regarding road safety, with myriad accidents transpiring annually, often culminating in fatalities or severe injuries. Conventional methods of accident detection heavily leaned on eyewitness testimonies, surveillance cameras with restricted angles, or tardy responses from emergency services, frequently leading to prolonged rescue durations and inadequate accident evaluations.
1.1.Early Development
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The preliminary phase of development entailed the aggregation of extensive datasets from diverse origins, encompassing traffic surveillance cameras, dashcams, and various video recording apparatuses, to train AI models. The primary focus revolved around formulating algorithms with the ability to concurrently scrutinize multiple perspectives of collision footage, discern recurring patterns, and promptly identify accidents as they unfold. This endeavor necessitated the utilization of sophisticated machine learning methodologies, notably deep learning, to effectively analyze and decipher the intricate data from varied vantage points.
1.2.Prototype and Testing By the early 2020s, the initial prototypes of "Safe Road AI" were developed. These prototypes underwent testing in controlled environments and later in real-world scenarios. The system was crafted to seamlessly integrate with existing infrastructure, such as traffic surveillance cameras, and had the capability to be incorporated into smart city frameworks. The AI was meticulously trained to identify various types of accidents, ranging from minor collisions to severe crashes, through real-time analysis of video feeds. A significant challenge encountered during this phase revolved around ensuring the precision and dependability of the system. The AI needed to exhibit the capacity to differentiate between genuine accidents and other similarlooking events, such as sudden braking or unexpected maneuvers. Continuous enhancements were implemented to the algorithms, which included augmenting the AI's aptitude to assimilate new data and refining its adaptability to diverse environmental conditions, such as varying weather patterns and lighting scenarios.
1.3.Implementation and Impact
The inception of "Safe Road AI" materialized in the latter part of the 2010s as advancements in artificial intelligence (AI), machine learning, and computer vision began to exhibit
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potential in the realm of real-time image and video processing. Scholars and technologists acknowledged the transformative capabilities of these technologies in enhancing road safety through prompt and precise accident detection, facilitating expedited response times and comprehensive data collection for analysis.
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"Safe Road AI" was progressively implemented in various urban centers globally by the mid-2020s. Its introduction
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