Scholarlink

India is the largest manufacturer of two-wheelers worldwide, accounting for over 75% of total automotive production, with approximately 271 million registered vehicles. Valued at USD 302.2 billion in 2022, the two-wheeler market is projected to reach USD 411.86 billion by 2032, growing at a CAGR of 3.50%. However, this sector also faces significant safety challenges, as two-wheelers were involved in nearly 39,396 fatalities in 2020, representing about 35.05% of total road accident deaths, predominantly affecting young individuals aged 18 to 35. A great contributor to these types of accidents is rider disbalancing while riding at low speed or maneuvering through tricky roads and traffic. Frequent foot-down events during slow-speed maneuvers and navigating through road obstacles highlight a rider’s struggle with balance, increasing their vulnerability to falls, collisions, or loss of control in real-world scenarios. This paper proposes a vision-based framework to assess the rider’s foot-down detection, incorporating a vision-based approach to determine if candidates touch the ground for support while riding a two wheeler. Additionally we created our own dataset of Indian two-wheelers comprising of motorbikes, scooters and moped from the commuter segment for this task.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine Vision-Based Real-Time Rider Foot-Down Detection for Indian Two Wheeler

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Jyoti Kale,
  • Harshal Verma,
  • Yashodhan Moghe,
  • Shruti Kudale,
  • P. Rajalakshmi

摘要

India is the largest manufacturer of two-wheelers worldwide, accounting for over 75% of total automotive production, with approximately 271 million registered vehicles. Valued at USD 302.2 billion in 2022, the two-wheeler market is projected to reach USD 411.86 billion by 2032, growing at a CAGR of 3.50%. However, this sector also faces significant safety challenges, as two-wheelers were involved in nearly 39,396 fatalities in 2020, representing about 35.05% of total road accident deaths, predominantly affecting young individuals aged 18 to 35. A great contributor to these types of accidents is rider disbalancing while riding at low speed or maneuvering through tricky roads and traffic. Frequent foot-down events during slow-speed maneuvers and navigating through road obstacles highlight a rider’s struggle with balance, increasing their vulnerability to falls, collisions, or loss of control in real-world scenarios. This paper proposes a vision-based framework to assess the rider’s foot-down detection, incorporating a vision-based approach to determine if candidates touch the ground for support while riding a two wheeler. Additionally we created our own dataset of Indian two-wheelers comprising of motorbikes, scooters and moped from the commuter segment for this task.