High-speed object tracking holds significant relevance across robotic domains, such as drones and autonomous driving. Compared to conventional cameras, event cameras are equipped with the ability to capture object motion information at exceptionally high temporal resolution with relatively low power consumption and remain immune from motion-blurring effects. Regrettably, many existing methods adopt a framebased approach by stacking events into Event Frame, which overlooks the sparsity and high temporal resolution of events. This approach is also reliant on the huge pre-training backbone and reaches a performance plateau but demands unrealistically large networks and high power consumption, rendering it impractical for real-time applications in battery-constrained robotic scenarios. In this paper, we propose an efficient and effective single-modality tracker using Point Cloud representation named E2B (Event to Box). By directly handling the raw output of event cameras without dataformat transformation, E2B leverages events' coordinate guidance to accurately map Event Cloud features to 2D bounding boxes. Moreover, E2B incorporates the pyramid structure into the multi-stage feature extraction architecture to effectively track objects across diverse scales. In the experiments, E2B performs outstandingly on two large-scale and one synthetic event-based tracking datasets, covering both indoor and outdoor environments, as well as rigid and non-rigid objects.

Recently, spiking neural networks (SNNs), deployed on neuromorphic chips, provide highly efficient solutions on edge devices in different scenarios. However, their ability to adapt to distribution shifts after deployment has become a crucial challenge. Online test-time adaptation (OTTA) offers a promising solution by enabling models to dynamically adjust to new data distributions without requiring source data or labeled target samples. Nevertheless, existing OTTA methods are largely designed for traditional artificial neural networks and are not well-suited for SNNs. To address this gap, we propose a low-power, neuromorphic chip-friendly online test-time adaptation framework, aiming to enhance model generalization under distribution shifts. The proposed approach is called Threshold Modulation (TM), which dynamically adjusts the firing threshold through neuronal dynamics-inspired normalization, being more compatible with neuromorphic hardware. Experimental results on benchmark datasets demonstrate the effectiveness of this method in improving the robustness of SNNs against distribution shifts while maintaining low computational cost. The proposed method offers a practical solution for online test-time adaptation of SNNs, providing inspiration for the design of future neuromorphic chips. The demo code is available at github.com/NneurotransmitterR/TM-OTTA-SNN.