论文标题
运动摄像头运动稳健的高速轻加权对象检测
Motion Robust High-Speed Light-Weighted Object Detection With Event Camera
论文作者
论文摘要
在这项工作中，我们提出了一项运动稳健和高速检测管道，可以更好地利用事件数据。首先，我们设计了一个称为时间活动焦点（TAF）的事件流表示，该表示有效地利用了空间异步事件流，从而为对象运动构建了鲁棒的事件张量。然后，我们提出了一个称为分叉的折叠模块（BFM）的模块，该模块在检测器的输入层的TAF张量中编码了丰富的时间信息。此后，我们设计了一个称为敏捷事件检测器（AED）的高速轻质检测器以及一种简单但有效的数据增强方法，以提高检测准确性并降低模型的参数。在两个典型的实物事件摄像机对象检测数据集上进行的实验表明，我们的方法在准确性，效率和参数数方面具有竞争力。通过基于光流密度度量的对象分类为多个运动级别，我们进一步说明了相对于相机，具有不同速度的对象的方法的鲁棒性。代码和训练有素的模型可在https://github.com/harmonialeo/frlw-evd上找到。
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 .
