Abstract
Event cameras which transmit per-pixel intensity changes have emerged as a promising candidate in applications such as consumer electronics, industrial automation, and autonomous vehicles, owing to their efficiency and robustness. To maintain these inherent advantages, the trade-off between efficiency and accuracy stands as a priority in event-based algorithms. Thanks to the preponderance of deep learning techniques and the compatibility between bio-inspired spiking neural networks and event-based sensors, data-driven approaches have become a hot spot, which along with the dedicated hardware and datasets constitute an emerging field named event-based data-driven technology. Focusing on data-driven technology in event-based vision, this paper first explicates the operating principle, advantages, and intrinsic nature of event cameras, as well as background knowledge in event-based vision, presenting an overview of this research field. Then, we explain why event-based data-driven technology becomes a research focus, including reasons for the rise of event-based vision and the superiority of data-driven approaches over other event-based algorithms. Current status and future trends of event-based data-driven technology are presented successively in terms of hardware, datasets, and algorithms, providing guidance for future research. Generally, this paper reveals the great prospects of event-based data-driven technology and presents a comprehensive overview of this field, aiming at a more efficient and bio-inspired visual system to extract visual features from the external environment.
Highlights
In event-based visual systems comprising perception and processing, algorithms in the processing part are designed to maintain the intrinsic advantages of event cameras, among which data-driven approaches are basically the most prevalent and promising algorithms
A visual system that can perceive the external environment and extract visual features of interest from it acts as a crucial prerequisite for performing specific tasks
Compared with traditional cameras that capture whole frames at a fixed rate, event cameras generate event data at a different data form. e novel working principle naturally leads to unique advantages and inherent properties of event data
Summary
In event-based visual systems comprising perception and processing, algorithms in the processing part are designed to maintain the intrinsic advantages of event cameras, among which data-driven approaches are basically the most prevalent and promising algorithms. Both current development and future trends of datadriven technology in event-based vision are discussed in terms of algorithms, hardware, and datasets’ field. Inspired by traditional frame-based vision and biological mechanisms, a new type of visual sensor named event camera [1, 2] is on the rise, aiming at applications where traditional cameras fail Equipped with merits such as high dynamic range, high temporal resolution, low latency, and low Complexity.
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