Deep Event Processing: From Microsecond Spatio-Temporal Streams to Aerial Target Forecasting

The proliferation of small unmanned aerial vehicles (UAVs) introduces critical surveillance and airspace security challenges that conventional frame-based optical systems struggle to resolve. Standard RGB sensors are fundamentally constrained by fixed exposure periods, which induce severe motion blur when capturing agile, high-velocity targets, while their limited dynamic range leads to information loss under low-light or backlit sky conditions. Neuromorphic vision sensors, or event cameras, present an asynchronous sensing paradigm that overcomes these degradation mechanisms by reporting per-pixel log-intensity variations with microsecond-level temporal precision and high dynamic range exceeding 120 dB. By eliminating synchronous frame exposure, event sensors suppress static visual backgrounds and encode high-frequency kinematic patterns without blur, drastically diminishing data throughput and transmission latency. These sensor characteristics unlock extensive methodological possibilities across aerial perception pipelines, spanning low-latency detection, multi-target tracking, trajectory forecasting, and state estimation. The ultra-fine temporal resolution captures the micro-motion signatures of high-speed rotating propellers, enabling frequency-domain tachometry, orientation estimation, and fine-grained classification against biological distractors such as birds and insects. Crucially, the sparse spatio-temporal streams facilitate diverse processing paradigms. Asynchronous event data can be discretized into dense voxel grids, compressed into time surfaces, or directly ingested as continuous four-dimensional point clouds via permutation-invariant neural backbones and graph architectures, avoiding temporal quantization artifacts. Furthermore, deploying event streams onto spiking neural networks and neuromorphic processors realizes end-to-end edge inference under strict power constraints. When paired with traditional optical streams in multimodal architectures, event data effectively compensates for the dynamic and illumination failures of standard imagery, while RGB features supply stationary spatial context. Neuromorphic sensing establishes a foundational architecture for real-time, low-power counter-UAV infrastructure capable of continuous tracking, intent prediction, and aerial threat mitigation in unstructured operational domains.