The problem of multi-object tracking (MOT) consists in detecting and tracking
all the objects in a video sequence while keeping a unique identifier for each
object. It is a challenging and fundamental problem for robotics. In precision
agriculture the challenge of achieving a satisfactory solution is amplified by
extreme camera motion, sudden illumination changes, and strong occlusions. Most
modern trackers rely on the appearance of objects rather than motion for
association, which can be ineffective when most targets are static objects with
the same appearance, as in the agricultural case. To this end, on the trail of
SORT [5], we propose AgriSORT, a simple, online, real-time
tracking-by-detection pipeline for precision agriculture based only on motion
information that allows for accurate and fast propagation of tracks between
frames. The main focuses of AgriSORT are efficiency, flexibility, minimal
dependencies, and ease of deployment on robotic platforms. We test the proposed
pipeline on a novel MOT benchmark specifically tailored for the agricultural
context, based on video sequences taken in a table grape vineyard, particularly
challenging due to strong self-similarity and density of the instances. Both
the code and the dataset are available for future comparisons.

基于运动信息的用于精准农业的实时检测跟踪管道的研究，重点是多目标追踪、精准农业、运动信息、检测跟踪管道和农业环境。

AgriSORT: 简易的在线实时检测跟踪框架用于农业精准机械

AgriSORT: A Simple Online Real-time Tracking-by-Detection framework for  robotics in precision agriculture

3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work
often uses a tracking-by-detection pipeline, where the feature of each object
is extracted independently to compute an affinity matrix. Then, the affinity
matrix is passed to the Hungarian algorithm for data association. A key process
of this pipeline is to learn discriminative features for different objects in
order to reduce confusion during data association. To that end, we propose two
innovative techniques: (1) instead of obtaining the features for each object
independently, we propose a novel feature interaction mechanism by introducing
Graph Neural Networks; (2) instead of obtaining the features from either 2D or
3D space as in prior work, we propose a novel joint feature extractor to learn
appearance and motion features from 2D and 3D space. Through experiments on the
KITTI dataset, our proposed method achieves state-of-the-art 3D MOT
performance. Our project website is at
this http URL

本研究提出了一种基于图神经网络和联合特征提取的 3D 多物体跟踪算法，通过在 KITTI 数据集上的实验验证，取得了最先进的 3D MOT 性能。