Adaptive Gradient-Based Timesurface for Event-based Detection

The advantages of high temporal resolution and high dynamic range provided by event cameras are particularly suitable for moving object detection, especially in scenarios with motion blur and extreme lighting conditions. Current popular methods predominantly focus on designing powerful network architectures to extract event features, often neglecting the rationality of event representation design which has been proven to impact significantly on downstream tasks. In particular, current event representations typically rely on fixed hyperparameters, without considering variations in relative motion speed, a key factor in motion-rich scenes captured by event cameras. To tackle this challenge, we propose a gradient-based scaled Timesurface (STS) motivated by the observation of the relationship between motion speeds and the gradient strength, which adaptively rescales the decay factor at different spatial positions. Additionally, we propose a dataset called RotateDigit, which is the first event dataset featuring clear motion level annotations to our best knowledge. Proposed STS method is verified using Spiking Neural Network (SNNs) due to the sharing asynchronous and sparse properties with event camera. Experimental results on RotateDigit and Gen1 show the performance improvement achieved by STS, which validates the rationality and effectiveness of our work.