An Efficient Hybrid Cascade Tracker with Spiking Neural Networks for Event Domain Tracking

Event cameras with high dynamic range and temporal resolution, which are bio-inspired vision sensors, have shown great potential in event-based tracking tasks, particularly in scenarios involving rapid motion and low levels of illumination. Nonetheless, the efficient extraction of sparse information from event camera remains a persistent challenge. Meanwhile, the event camera works asynchronously, generating a continuous stream of events, rendering it highly compatible with Spiking Neural Networks (SNNs) due to their event-driven nature and low power consumption. Motivated by the issues mentioned above, we propose an E fficient H ybrid C ascade T racker with SNN for object tracking in the event domain, called EHCT . We combine the transformer, convolutional network and SNN structure skillfully to form the basic Hybrid CNN-SNN-Transformer (HCST) block structure, which is also the central component part of our EHCT network. The HCST block is primarily utilized to process the incoming event data and extract information from both local and global contexts. After several cascade HCST blocks, these two types of information will be efficiently integrated with the preprocessed raw data, which are then fed into the Classifier and Regressor head to produce the bounding box (bbox) of the tracked target. Extensive experiments on various event and RGB frame-based datasets demonstrated that our proposed EHCT algorithm outperforms most of the existing state-of-the-art trackers by a significant margin and also achieves a great advantage in terms of energy consumption. Our source code will be available on Here .