End-to-End Learning of Representations for Asynchronous Event-Based Data (1904.08245v4)

Abstract: Event cameras are vision sensors that record asynchronous streams of per-pixel brightness changes, referred to as "events". They have appealing advantages over frame-based cameras for computer vision, including high temporal resolution, high dynamic range, and no motion blur. Due to the sparse, non-uniform spatiotemporal layout of the event signal, pattern recognition algorithms typically aggregate events into a grid-based representation and subsequently process it by a standard vision pipeline, e.g., Convolutional Neural Network (CNN). In this work, we introduce a general framework to convert event streams into grid-based representations through a sequence of differentiable operations. Our framework comes with two main advantages: (i) allows learning the input event representation together with the task dedicated network in an end to end manner, and (ii) lays out a taxonomy that unifies the majority of extant event representations in the literature and identifies novel ones. Empirically, we show that our approach to learning the event representation end-to-end yields an improvement of approximately 12% on optical flow estimation and object recognition over state-of-the-art methods.

• The paper introduces an innovative framework that transforms asynchronous event streams into grid-based representations, achieving a 12% improvement over previous methods.
• It emphasizes the importance of retaining both event polarity and temporal details to enhance tasks like optical flow estimation and object recognition.
• End-to-end learned kernels outperform traditional heuristic-based approaches, paving the way for efficient, real-time processing in autonomous systems.

End-to-End Learning of Representations for Asynchronous Event-Based Data

The discussed paper presents a novel approach to handling data from event cameras, which capture asynchronous per-pixel brightness changes instead of fixed rate images. Event cameras offer key advantages, such as high temporal resolution, high dynamic range, and elimination of motion blur, which positions them as suitable alternatives for frame-based cameras, particularly in challenging conditions with rapid motion or extreme lighting variability.

The main contribution of this paper is a framework for transforming event streams into grid-based representations via a sequence of differentiable operations, enabling end-to-end learning. This method underpins improved performance in tasks such as optical flow estimation and object recognition, demonstrating a 12% improvement over prevailing state-of-the-art techniques. The core innovation lies in the flexibility of learning both the event representation and the task network simultaneously, which not only enhances accuracy but also facilitates the discovery of novel event representations.

The authors delineate a theoretical taxonomy, which unifies existing approaches to event data representation and introduces new ones, while distinguishing between continuous-time and packet-based processes. The most promising representation, termed the Event Spike Tensor (EST), maintains event polarity and temporal localization, thus maximizing the information retention from the raw event stream. The paper does not merely stop at theoretical formulations; it provides empirical evidence corroborating the efficacy of this approach on standard benchmarks like the N-Cars dataset and the MVSEC dataset, underscoring the practical relevance of the proposed method.

The extensive analysis of the impact of representations and kernel functions on task performance is pivotal. It highlights that retaining both polarity and time information is crucial for object classification tasks, whereas for optical flow estimation, temporal information emerges as the more critical element. Furthermore, the paper finds that end-to-end learned kernels outperform traditional heuristic-based kernels, confirming the advantage of allowing the model to derive representations tailored to the specific task.

In terms of future prospects, this research opens pathways for deploying advanced learning algorithms directly onto event camera data. While the current framework processes events in packets for increased accuracy, there is potential for convergence to an efficient, asynchronous processing paradigm via recurrent architectures. Such developments could bridge the gap between high accuracy and low latency requirements, broadening application horizons in areas like autonomous navigation and robotics.

Overall, this paper outlines a significant stride forward in the utilization of event cameras, leveraging modern machine learning paradigms to harness the full spectrum of their intrinsic advantages. By addressing the previous knowledge gap concerning optimal event stream representation, it provides a robust foundation for ongoing and future research in asynchronous event-based data processing.