CampusDepth-A-Large-Scale-Day-Night-RGB-Dataset-for-Monocular-Depth-Estimation

Description

Advanced Driver Assistance Systems (ADAS) require accurate and reliable perception of the surrounding environment to ensure vehicle safety and reduce the risk of collisions. Depth estimation plays a crucial role in understanding object distance and spatial relationships in traffic scenes. Traditional depth sensing approaches, such as stereo camera systems and LiDAR, provide accurate depth information but suffer from high cost, increased hardware complexity, calibration requirements, and limited suitability for low-cost embedded platforms. In recent years, monocular depth estimation using a single RGB camera has emerged as a promising alternative due to significant advancements in deep learning and computer vision techniques. This work focuses on the preliminary data analysis required for the development of a low-cost, camera-based anti-collision system using monocular depth estimation to address the limitations of conventional depth sensing methods. The study emphasizes the importance of conducting a comprehensive statistical and performance analysis on large-scale datasets collected across a finite spatial domain within the campus environment. The analysis aims to identify data trends, evaluate depth prediction consistency, and understand scenario-specific performance variations under different environmental and traffic conditions. Furthermore, the proposed methodology evaluates the collected campus dataset under Indian road conditions to examine the adaptability, robustness, and calibration requirements of the monocular depth estimation framework. The findings from this preliminary analysis are intended to support the efficient implementation, optimization, and future deployment of the proposed anti-collision system in real-world driving scenarios.

Steps to reproduce

The images can be taken for training and testing purposes of different Machine Learning, Deep Learning models for vehicular networks and intelligent transportation systems. The dataset is focused on monocular camera images. Alternatively it can also be used for object detection and classification purposes.

Institutions

• Amrita Vishwa Vidyapeetham

  Tamil Nadu, Coimbatore

Categories

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