# Motion Robust High-Speed Light-Weighted Object Detection With Event Camera Source: [Motion Robust High-Speed Light-Weighted Object Detection With Event Camera]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:27:18.619Z Authors: Bingde Liu, Chang Xu, Wen Yang, Huai Yu, Lei Yu Published: 2022-08-24T15:15:24.000Z ## Abstract In this work, we propose a motion robust and high-speed detection pipeline which better leverages the event data. First, we design an event stream representation called temporal active focus (TAF), which efficiently utilizes the spatial-temporal asynchronous event stream, constructing event tensors robust to object motions. Then, we propose a module called the bifurcated folding module (BFM), which encodes the rich temporal information in the TAF tensor at the input layer of the detector. Following this, we design a high-speed lightweight detector called agile event detector (AED) plus a simple but effective data augmentation method, to enhance the detection accuracy and reduce the model's parameter. Experiments on two typical real-scene event camera object detection datasets show that our method is competitive in terms of accuracy, efficiency, and the number of parameters. By classifying objects into multiple motion levels based on the optical flow density metric, we further illustrated the robustness of our method for objects with different velocities relative to the camera. The codes and trained models are available at https://github.com/HarmoniaLeo/FRLW-EvD . ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on DSEC-Detection 10 (test)](): Object Detection; DSEC-Detection 10 (test); mAP: 27.1. ## Original sources and implementation [Original paper]() [PDF]()