HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification

TL;DR

This paper introduces a novel event-based feature representation and architecture for object classification, along with a large real-world dataset, significantly improving accuracy and efficiency over previous methods.

Contribution

The paper presents a new histogram-based feature representation and a machine learning architecture for event-based classification, and releases the first large real-world dataset for this task.

Findings

Outperforms state-of-the-art in classification accuracy

Enables real-time processing of event-based data

Provides a large dataset for future research

Abstract

Event-based cameras have recently drawn the attention of the Computer Vision community thanks to their advantages in terms of high temporal resolution, low power consumption and high dynamic range, compared to traditional frame-based cameras. These properties make event-based cameras an ideal choice for autonomous vehicles, robot navigation or UAV vision, among others. However, the accuracy of event-based object classification algorithms, which is of crucial importance for any reliable system working in real-world conditions, is still far behind their frame-based counterparts. Two main reasons for this performance gap are: 1. The lack of effective low-level representations and architectures for event-based object classification and 2. The absence of large real-world event-based datasets. In this paper we address both problems. First, we introduce a novel event-based feature representation together with a new machine learning architecture. Compared to previous approaches, we use local memory units to efficiently leverage past temporal information and build a robust event-based representation. Second, we release the first large real-world event-based dataset for object classification. We compare our method to the state-of-the-art with extensive experiments, showing better classification performance and real-time computation.