Recent advances of monocular 3D object detection facilitate the 3D
multi-object tracking task based on low-cost camera sensors. In this paper, we
find that the motion cue of objects along different time frames is critical in
3D multi-object tracking, which is less explored in existing monocular-based
approaches. In this paper, we propose a motion-aware framework for monocular 3D
MOT. To this end, we propose MoMA-M3T, a framework that mainly consists of
three motion-aware components. First, we represent the possible movement of an
object related to all object tracklets in the feature space as its motion
features. Then, we further model the historical object tracklet along the time
frame in a spatial-temporal perspective via a motion transformer. Finally, we
propose a motion-aware matching module to associate historical object tracklets
and current observations as final tracking results. We conduct extensive
experiments on the nuScenes and KITTI datasets to demonstrate that our MoMA-M3T
achieves competitive performance against state-of-the-art methods. Moreover,
the proposed tracker is flexible and can be easily plugged into existing
image-based 3D object detectors without re-training. Code and models are
available at this https URL

提出了一种基于运动感知的单目三维多目标跟踪（MoMA-M3T）框架，通过在时间帧内捕捉物体的运动线索，将历史物体轨迹与当前观察结果进行匹配，实现与最先进方法相媲美的性能。

深入研究单目三维物体跟踪中的动作感知匹配

Delving into Motion-Aware Matching for Monocular 3D Object Tracking

Recent camera-based 3D object detection methods have introduced sequential
frames to improve the detection performance hoping that multiple frames would
mitigate the large depth estimation error. Despite improved detection
performance, prior works rely on naive fusion methods (e.g., concatenation) or
are limited to static scenes (e.g., temporal stereo), neglecting the importance
of the motion cue of objects. These approaches do not fully exploit the
potential of sequential images and show limited performance improvements. To
address this limitation, we propose a novel 3D object detection model, P2D
(Predict to Detect), that integrates a prediction scheme into a detection
framework to explicitly extract and leverage motion features. P2D predicts
object information in the current frame using solely past frames to learn
temporal motion features. We then introduce a novel temporal feature
aggregation method that attentively exploits Bird's-Eye-View (BEV) features
based on predicted object information, resulting in accurate 3D object
detection. Experimental results demonstrate that P2D improves mAP and NDS by
3.0% and 3.7% compared to the sequential image-based baseline, illustrating
that incorporating a prediction scheme can significantly improve detection
accuracy.

本文提出了一种名为 P2D 的新型三维物体检测模型，它将预测方案整合到检测框架中，显式提取和利用运动特征，以及引入基于预测物体信息的 Bird's-Eye-View（BEV）特征的新型时间特征聚合方法，结果表明，与基线相比，P2D 可以显著提高检测精度。

预测为基础的三维物体检测：使用连续图像

Predict to Detect: Prediction-guided 3D Object Detection using  Sequential Images

Efficiently modeling dynamic motion information in videos is crucial for
action recognition task. Most state-of-the-art methods heavily rely on dense
optical flow as motion representation. Although combining optical flow with RGB
frames as input can achieve excellent recognition performance, the optical flow
extraction is very time-consuming. This undoubtably will count against
real-time action recognition. In this paper, we shed light on fast action
recognition by lifting the reliance on optical flow. Our motivation lies in the
observation that small displacements of motion boundaries are the most critical
ingredients for distinguishing actions, so we design a novel motion cue called
Persistence of Appearance (PA). In contrast to optical flow, our PA focuses
more on distilling the motion information at boundaries. Also, it is more
efficient by only accumulating pixel-wise differences in feature space, instead
of using exhaustive patch-wise search of all the possible motion vectors. Our
PA is over 1000x faster (8196fps vs. 8fps) than conventional optical flow in
terms of motion modeling speed. To further aggregate the short-term dynamics in
PA to long-term dynamics, we also devise a global temporal fusion strategy
called Various-timescale Aggregation Pooling (VAP) that can adaptively model
long-range temporal relationships across various timescales. We finally
incorporate the proposed PA and VAP to form a unified framework called
Persistent Appearance Network (PAN) with strong temporal modeling ability.
Extensive experiments on six challenging action recognition benchmarks verify
that our PAN outperforms recent state-of-the-art methods at low FLOPs. Codes
and models are available at: this https URL

本文针对实时动作识别应用，提出了一种新的运动线索 —— 外观持续性（PA），它通过局部像素差异累加而不是大量的运动向量搜索来提取动作信息，并基于 PA 提出了一种新的全局时间融合方案 —— 各时间尺度汇聚池化（VAP），从而构建了一个统一的框架 —— 持续外观网络（PAN），该方法在 6 个挑战性动作识别基准测试中表现出优异的动态建模能力。