Event Data Representation Based on Spatiotemporal Neighborhood-Associated Denoising Time Surfaces

0
0
0

Event Data Representation Based on Spatiotemporal Neighborhood-Associated Denoising Time Surfaces

Introduction

Event cameras, inspired by biological vision systems, have emerged as a revolutionary sensing technology due to their ultra-high dynamic range and microsecond-level response latency. Unlike conventional frame-based cameras, event cameras asynchronously capture per-pixel brightness changes, making them ideal for high-speed and high-dynamic-range applications such as autonomous driving, robotics, and surveillance. However, processing the sparse and asynchronous event streams efficiently remains a significant challenge. Existing methods for event representation often suffer from high redundancy and susceptibility to noise, limiting their effectiveness in real-world applications.

This paper introduces a novel approach to event data representation that addresses these challenges through two key innovations: (1) a density-based event downscaling algorithm that reduces computational overhead while preserving critical spatiotemporal information, and (2) a spatiotemporal neighborhood-associated denoising time surface (STDTS) method that enhances signal-to-noise ratio and improves classification accuracy. The proposed techniques are evaluated on three widely used neuromorphic datasets—CIFAR10-DVS, DVS128 Gesture, and N-Caltech 101—demonstrating state-of-the-art (SOTA) performance in object classification tasks.

Background and Motivation

Event cameras generate data in the form of discrete events, each containing spatial coordinates, a timestamp, and polarity (indicating brightness increase or decrease). While this sparse representation enables low-latency and energy-efficient sensing, extracting meaningful spatiotemporal features from event streams is non-trivial. Traditional event processing pipelines involve two main stages: event stream segmentation/filtering and event representation.

Existing segmentation methods, such as fixed-time or fixed-event-count partitioning, struggle with dynamic scenes where motion speeds vary significantly. Similarly, event filtering techniques like spatial downscaling or event counting either discard essential spatial details or fail to account for higher-level spatiotemporal correlations. On the representation side, methods like time surfaces, event spike tensors, and voxel grids attempt to encode temporal and spatial event relationships but often introduce noise sensitivity or computational inefficiencies.

The limitations of current approaches motivate the need for a more robust event representation framework that:

  1. Reduces event redundancy without losing critical information.
  2. Mitigates noise by leveraging spatiotemporal neighborhood relationships.
  3. Maintains computational efficiency for real-time applications.

Methodology

The proposed framework consists of four main modules: adaptive event stream segmentation, density-based event downscaling, spatiotemporal neighborhood-associated denoising time surfaces, and classification using a spiking neural network (SNN).

Event Stream Processing

Adaptive Event Stream Segmentation

To handle varying event rates in dynamic scenes, the method employs an adaptive event sampling strategy. Starting from the initial timestamp of each labeled segment, events are accumulated in a spatiotemporal window until the count approaches a dynamically adjusted threshold. This threshold incorporates a feedback control parameter to maintain temporal consistency across segments. The adaptive approach ensures that segments are neither too sparse (losing temporal resolution) nor too dense (increasing computational load).

Density-Based Event Downscaling

Given the high event throughput of event cameras (up to 25 million events per second), reducing computational overhead is essential. Instead of naive spatial downscaling, which may discard important edge details, the proposed method analyzes the spatial density distribution of events.

  1. Kernel Density Estimation: A Gaussian kernel is applied to estimate the spatial density of events within each segment. This step identifies regions with high event concentration, typically corresponding to salient objects or motion.
  2. Density-Based Sorting and Selection: Events are sorted by their estimated density values, and only the top R% (e.g., 33% based on empirical validation) are retained. This selective downscaling preserves high-density regions where critical information resides while discarding sparse, less informative events.

Experiments on CIFAR10-DVS and DVS128 Gesture datasets confirm that retaining one-third of events optimizes the trade-off between computational efficiency and classification accuracy.

Spatiotemporal Neighborhood-Associated Denoising Time Surfaces

Event Clustering via Spatiotemporal Neighborhoods

Noise events in event streams tend to exhibit spatiotemporal isolation compared to valid events. To filter noise, the method groups events into clusters based on their local spatiotemporal relationships:
• A neighborhood is defined as a circular region with radius ε around each event.

• Events are classified as core points if their neighborhood contains at least a minimum number of neighboring events (min_samples).

• Direct and indirect neighborhood relationships are established to form contiguous clusters.

• Clusters with fewer than N_s events are discarded as noise.

This clustering effectively isolates valid event patterns while suppressing spurious noise events.

Denoising Time Surface Computation

Traditional time surfaces compute a single exponential kernel response based on the time difference between events, making them sensitive to noise and minor event variations. The proposed method enhances robustness by:

  1. Multi-Kernel Time Surface: Instead of a single exponential kernel, a pair of balanced positive and negative kernels are used. The combined response emphasizes events with varying temporal frequencies while canceling out constant-rate noise.
  2. Spatiotemporal Context Integration: Only events belonging to valid clusters contribute to the time surface calculation. This ensures that the representation reflects coherent spatiotemporal patterns rather than isolated noise events.

The resulting time surface is more discriminative and less susceptible to noise, improving feature extraction for downstream tasks like classification.

Experiments and Results

Datasets and Evaluation Metrics

The method is evaluated on three benchmark datasets:

  1. CIFAR10-DVS: A 10-class object recognition dataset with 128×128 resolution, converted from CIFAR-10 images.
  2. DVS128 Gesture: An 11-class gesture recognition dataset recorded with a DVS camera.
  3. N-Caltech 101: A 101-class dataset captured with an ATIS event camera, derived from Caltech 101.

Classification accuracy is used as the primary metric, calculated as the ratio of correctly classified samples to the total test set size.

Implementation Details

Training is conducted on an NVIDIA A100 GPU using the Adam optimizer with a batch size of 10 and a learning rate of 0.001. The SNN architecture follows a VGG-11 style network with alternating convolutional and pooling layers, culminating in a fully connected output layer. The loss function is spike mean-square-error (SMSE), which penalizes deviations between the network’s output spikes and the target labels.

Ablation Studies

Event Downscaling Comparison

On the DVS128 Gesture dataset, the proposed density-based downscaling reduces the average number of events from 294,218 to 98,106 (66% reduction) while achieving 99.0% classification accuracy. In contrast, prior methods like Simple Event Funneling (SEF) and Tonic downscaling achieve only 55.3% and 63.2% accuracy, respectively, with higher computational latency.

Time Surface Variants

The denoising time surface outperforms existing time surface methods across all datasets:
• DVS128 Gesture: 99.1% accuracy (vs. 92.8% for HOTS and 95.6% for HATS).

• CIFAR10-DVS: 81.3% accuracy (vs. 27.1% for HOTS and 52.4% for HATS).

• N-Caltech 101: 82.1% accuracy (vs. 28.2% for HOTS and 56.5% for HATS).

The improvement stems from better noise suppression and spatiotemporal feature preservation.

Comparison with State-of-the-Art

The full pipeline (STDTS) achieves SOTA results:
• N-Caltech 101: 85.2% accuracy (vs. 81.7% for EST and 84.2% for IETS).

• CIFAR10-DVS: 84.3% accuracy (vs. 74.9% for EST and 73.8% for IETS).

• DVS128 Gesture: 99.1% accuracy (vs. 96.2% for TORE and 97.3% for STES).

Notably, STDTS maintains low computational latency even with large event counts, making it suitable for real-time applications.

Conclusion

This paper presents a comprehensive solution for efficient and accurate event data representation. By combining density-based event downscaling with spatiotemporal neighborhood-aware denoising, the method significantly reduces computational overhead while improving classification performance. Experimental results on three neuromorphic datasets validate the superiority of the approach over existing techniques. Future work may explore adaptive parameter tuning for diverse applications and integration with larger-scale SNN architectures.

doi.org/10.19734/j.issn.1001-3695.2024.04.0117

Was this helpful?

0 / 0