用图像化轨迹+2D CNN实现运动模式分类与定量分析
Classification and regression of trajectories rendered as images via 2D Convolutional Neural Networks
- 将轨迹转为图像后输入2D CNN，自动学习空间特征
- 高分辨率与运动历史编码显著提升方向敏感任务性能
- 适合需要分析运动方向的生物追踪或轨迹预测场景
轨迹可视为坐标的时间序列，常见于移动物体。轨迹分类有助于识别不同运动模式，回归分析则用于计算运动度量和预测。近年来计算机视觉进展使基于二维卷积神经网络（CNN）处理图像化轨迹成为可能。该方法利用CNN学习图像中复杂形状的空间层次特征，克服了传统机器学习对轨迹点数固定的限制。然而，轨迹图像化过程可能引入未充分研究的伪影，如离散网格导致的信息损失，以及线宽和混叠引起的频谱变化。本研究系统评估了在不同模态下（包括线宽、图像分辨率、运动历史编码、抗混叠）使用CNN解决合成轨迹的分类与回归问题的有效性。结果表明，图像分辨率需根据模型深度及是否使用运动历史进行合理选择，尤其在依赖运动方向的任务中尤为重要。
原文摘要 · Abstract (English)
Trajectories can be regarded as time-series of coordinates, typically arising from motile objects. Methods for trajectory classification are particularly important to detect different movement patterns, while methods for regression to compute motility metrics and forecasting. Recent advances in computer vision have facilitated the processing of trajectories rendered as images via artificial neural networks with 2d convolutional layers (CNNs). This approach leverages the capability of CNNs to learn spatial hierarchies of features from images, necessary to recognize complex shapes. Moreover, it overcomes the limitation of other machine learning methods that require input trajectories with a fixed number of points. However, rendering trajectories as images can introduce poorly investigated artifacts such as information loss due to the plotting of coordinates on a discrete grid, and spectral changes due to line thickness and aliasing. In this study, we investigate the effectiveness of CNNs for solving classification and regression problems from synthetic trajectories that have been rendered as images using different modalities. The parameters considered in this study include line thickness, image resolution, usage of motion history (color-coding of the temporal component) and anti-aliasing. Results highlight the importance of choosing an appropriate image resolution according to model depth and motion history in applications where movement direction is critical.
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