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.

翻译：轨迹可视为运动物体产生的坐标时间序列。轨迹分类方法对于检测不同运动模式尤为重要，而回归方法则用于计算运动学指标与预测。计算机视觉领域的最新进展促进了通过二维卷积层人工神经网络（CNN）处理轨迹图像的方法。该技术利用CNN从图像中学习空间层次特征的能力，这对于识别复杂形状至关重要。此外，该方法克服了其他机器学习技术需要固定点数轨迹输入的限制。然而，将轨迹渲染为图像可能引入尚未充分研究的伪影，例如因坐标离散化绘图导致的信息损失，以及线宽和混叠效应引起的光谱变化。本研究通过不同渲染模式生成的合成轨迹图像，探究CNN在分类与回归问题中的有效性。实验参数包括线宽、图像分辨率、运动历史信息（时间分量的色彩编码）及抗锯齿处理。结果表明，在运动方向至关重要的应用场景中，需根据模型深度与运动历史特征选择适当的图像分辨率。