This paper introduces SFSORT, the world's fastest multi-object tracking
system based on experiments conducted on MOT Challenge datasets. To achieve an
accurate and computationally efficient tracker, this paper employs a
tracking-by-detection method, following the online real-time tracking approach
established in prior literature. By introducing a novel cost function called
the Bounding Box Similarity Index, this work eliminates the Kalman Filter,
leading to reduced computational requirements. Additionally, this paper
demonstrates the impact of scene features on enhancing object-track association
and improving track post-processing. Using a 2.2 GHz Intel Xeon CPU, the
proposed method achieves an HOTA of 61.7\% with a processing speed of 2242 Hz
on the MOT17 dataset and an HOTA of 60.9\% with a processing speed of 304 Hz on
the MOT20 dataset. The tracker's source code, fine-tuned object detection
model, and tutorials are available at
https://github.com/gitmehrdad/SFSORT.

该论文介绍了 SFSORT，这是基于 MOT Challenge 数据集上的实验而开发的世界上速度最快的多目标跟踪系统。通过引入一种新的成本函数，称为边界框相似性指数，该系统实现了准确且计算效率高的跟踪器，消除了卡尔曼滤波器，从而减少了计算需求，并展示了场景特征对增强物体跟踪关联和改进跟踪后处理的影响。使用 2.2 GHz Intel Xeon CPU，在 MOT17 数据集上，该方法实现了 61.7% 的 HOTA 和 2242 Hz 的处理速度，在 MOT20 数据集上，实现了 60.9% 的 HOTA 和 304 Hz 的处理速度。该跟踪器的源代码、优化的物体检测模型和教程可以在 https://github.com/gitmehrdad/SFSORT 上获取。

SFSORT：基于场景特征的简易在线实时跟踪器

SFSORT: Scene Features-based Simple Online Real-Time Tracker

Autonomous driving promises transformative improvements to transportation,
but building systems capable of safely navigating the unstructured complexity
of real-world scenarios remains challenging. A critical problem lies in
effectively predicting the various potential outcomes that may emerge in
response to the vehicle's actions as the world evolves.
To address this challenge, we introduce GAIA-1 ('Generative AI for
Autonomy'), a generative world model that leverages video, text, and action
inputs to generate realistic driving scenarios while offering fine-grained
control over ego-vehicle behavior and scene features. Our approach casts world
modeling as an unsupervised sequence modeling problem by mapping the inputs to
discrete tokens, and predicting the next token in the sequence. Emerging
properties from our model include learning high-level structures and scene
dynamics, contextual awareness, generalization, and understanding of geometry.
The power of GAIA-1's learned representation that captures expectations of
future events, combined with its ability to generate realistic samples,
provides new possibilities for innovation in the field of autonomy, enabling
enhanced and accelerated training of autonomous driving technology.

GAIA-1 ('Generative AI for Autonomy') 是一个生成性世界模型，利用视频、文本和动作输入生成逼真的驾驶场景，同时提供对自我车辆行为和场景特征的细粒度控制，通过将输入映射为离散标记并预测序列中的下一个标记来解决了在自动驾驶过程中有效预测可能出现的各种结果的挑战。该模型通过捕捉未来事件的预期和生成逼真样本的能力，提供了在自动驾驶技术领域创新的新可能性，实现了自动驾驶技术的增强和加速训练。

GAIA-1: 自动驾驶的生成式世界模型

GAIA-1: A Generative World Model for Autonomous Driving

Driving scene understanding is to obtain comprehensive scene information
through the sensor data and provide a basis for downstream tasks, which is
indispensable for the safety of self-driving vehicles. Specific perception
tasks, such as object detection and scene graph generation, are commonly used.
However, the results of these tasks are only equivalent to the characterization
of sampling from high-dimensional scene features, which are not sufficient to
represent the scenario. In addition, the goal of perception tasks is
inconsistent with human driving that just focuses on what may affect the
ego-trajectory. Therefore, we propose an end-to-end Interpretable Implicit
Driving Scene Understanding (II-DSU) model to extract implicit high-dimensional
scene features as scene understanding results guided by a planning module and
to validate the plausibility of scene understanding using auxiliary perception
tasks for visualization. Experimental results on CARLA benchmarks show that our
approach achieves the new state-of-the-art and is able to obtain scene features
that embody richer scene information relevant to driving, enabling superior
performance of the downstream planning.

通过传感器数据获取综合场景信息，并为下游任务提供基础，对于自动驾驶车辆的安全至关重要，本文提出了一种端到端的可解释性隐式驾驶场景理解模型，通过规划模块引导提取隐式高维场景特征作为场景理解结果，并使用辅助感知任务进行可视化验证，实验证明我们的方法在 CARLA 基准测试上达到了最新的最高水平，并能够获取体现与驾驶相关的更丰富的场景信息，实现下游规划的卓越性能。