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About this paper
Pratham G. Shenwai, Hemant Kumar Singh, Sridhar Ravi
A training-free framework for real-time unsupervised moving object discovery from asynchronous event camera streams is introduced, leveraging a spatio-temporal probabilistic filter (SPEF) for denoising and Morton code clustering (EMCC) for efficient object grouping. The system processes event segments 4.3x faster than previous methods, achieving 6.3 ms latency while improving object localization.
Key insights
- Leveraging the intrinsic spatio-temporal geometry of event data through 1D Morton codes can replace complex multi-dimensional neighborhood searches with efficient sorting and gap detection.
- A probabilistic event acceptance mechanism in denoising dynamically adapts to local spatio-temporal coherence, offering robustness without fixed thresholds or learned parameters.
- Combining density-based cluster validation with spatial aggregation of bounding box proposals effectively refines object boundaries and merges fragmented detections.
Summary by alphaXiv.
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