SpikeYoloV8-Tracker Introduction
SpikeYoloV8-Tracker is a complete end-to-end pipeline for real-time object detection and tracking using event camera data. The architecture is adapted from the BICLab SpikeYOLO ECCV 2024 implementation.
Key Features
- BICLab ECCV 2024 Implementation: Original SpikeYOLO with I-LIF spiking neurons
- Configurable-Class Detection: Detects multiple object classes dynamically
- Object Tracking: Tracks objects through time using Hungarian-algorithm based ByteTracker
- Event Processing: Converts event data to spike trains for SNN processing
- End-to-End Pipeline: Contains highly configurable training and testing pipeline
What is Event-Based Vision?
Event cameras (also known as neuromorphic cameras) are bio-inspired sensors that capture changes in brightness at each pixel independently, rather than capturing full frames at fixed intervals. This provides:
- High Temporal Resolution: Microsecond-level precision
- High Dynamic Range: >86 dB
- Low Latency: Event-by-event processing
- Energy Efficiency: Only processes changes in the scene
Use Cases
SpikeYoloV8-Tracker is ideal for:
- Traffic Monitoring: Real-time detection and tracking of vehicles, pedestrians, and other traffic participants
- Surveillance Systems: Low-power, high-speed object tracking
- Autonomous Systems: Event-based perception for robotics and autonomous vehicles
- Research: Spiking neural networks for event-based vision
Project Repository
The source code is available at: https://github.com/type1compute/SpikeYoloV8-Tracker