Poster + Paper
22 May 2023 Event-based object detection and recognition for driver vision enhancement
Author Affiliations +
Conference Poster
Abstract
Object detection is a critical task in computer vision, with applications ranging from robotics to surveillance. Traditional RGB-based methods often face challenges in low-light, high-speed, or high-dynamic-range scenarios, resulting in blurred or low-contrast images. In this paper, we present a novel algorithmic approach that fuses event data from event cameras with RGB images to improve object detection performance in real-time. Event cameras, unlike traditional frame-based cameras, provide high temporal resolution and dynamic range, capturing intensity changes asynchronously at the pixel level. Our method leverages the complementary strengths of event data and RGB images to reconstruct blurred images while retaining contrast information from the original data. We propose an algorithmic pipeline that first fuses event data and RGB images, followed by a reconstruction step to generate enhanced images suitable for object detection tasks. The pipeline does not rely on deep learning techniques, making it computationally efficient and well-suited for real-time applications. To validate the effectiveness of our approach, we compare its performance against the popular YOLO benchmarks for object detection tasks. Moreover, we assess real-time metrics to demonstrate the practicality of our method in time-sensitive applications.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hamad AlRemeithi, Issac Niwas Swamidoss, Abdulla Al Mansoori, Abdulrahman AlMarzooqi, Slim Sayadi, and Tarek Bouamer "Event-based object detection and recognition for driver vision enhancement", Proc. SPIE 12327, SPIE Future Sensing Technologies 2023, 1232723 (22 May 2023); https://doi.org/10.1117/12.2653224
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KEYWORDS
Object detection

Cameras

Tunable filters

RGB color model

Object recognition

Optical filters

Image restoration

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