Multi-object tracking (MOT) has profound applications in a variety of fields,
including surveillance, sports analytics, self-driving, and cooperative
robotics. Despite considerable advancements, existing MOT methodologies tend to
falter when faced with non-uniform movements, occlusions, and
appearance-reappearance scenarios of the objects. Recognizing this inadequacy,
we put forward an integrated MOT method that not only marries object detection
and identity linkage within a singular, end-to-end trainable framework but also
equips the model with the ability to maintain object identity links over long
periods of time. Our proposed model, named STMMOT, is built around four key
modules: 1) candidate proposal generation, which generates object proposals via
a vision-transformer encoder-decoder architecture that detects the object from
each frame in the video; 2) scale variant pyramid, a progressive pyramid
structure to learn the self-scale and cross-scale similarities in multi-scale
feature maps; 3) spatio-temporal memory encoder, extracting the essential
information from the memory associated with each object under tracking; and 4)
spatio-temporal memory decoder, simultaneously resolving the tasks of object
detection and identity association for MOT. Our system leverages a robust
spatio-temporal memory module that retains extensive historical observations
and effectively encodes them using an attention-based aggregator. The
uniqueness of STMMOT lies in representing objects as dynamic query embeddings
that are updated continuously, which enables the prediction of object states
with attention mechanisms and eradicates the need for post-processing.

提出了一个综合的多目标跟踪方法 STMMOT，该方法结合了目标检测和身份链接，能够在长时间内维持目标身份链接，并利用具有注意力机制的鲁棒时空记忆模块和动态查询嵌入来预测目标状态，消除了后处理的需求。

非受限环境下的多人跟踪和再识别的 Transformer 网络

Transformer Network for Multi-Person Tracking and Re-Identification in  Unconstrained Environment

We propose an online tracking algorithm that performs the object detection
and data association under a common framework, capable of linking objects after
a long time span. This is realized by preserving a large spatio-temporal memory
to store the identity embeddings of the tracked objects, and by adaptively
referencing and aggregating useful information from the memory as needed. Our
model, called MeMOT, consists of three main modules that are all
Transformer-based: 1) Hypothesis Generation that produce object proposals in
the current video frame; 2) Memory Encoding that extracts the core information
from the memory for each tracked object; and 3) Memory Decoding that solves the
object detection and data association tasks simultaneously for multi-object
tracking. When evaluated on widely adopted MOT benchmark datasets, MeMOT
observes very competitive performance.

该文提出了一种名为 MeMOT 的在线跟踪算法，基于 Transformer 框架实现目标检测、数据关联和多目标跟踪，并采用存储身份嵌入的大型时空记忆，能在长时间跨度后链接对象，经过基准数据集的测试，MeMOT 表现出极具竞争力的性能。