  This study proposes an improved end-to-end multi-target tracking algorithm
that adapts to multi-view multi-scale scenes based on the self-attentive
mechanism of the transformer's encoder-decoder structure. A multi-dimensional
feature extraction backbone network is combined with a self-built semantic
raster map, which is stored in the encoder for correlation and generates target
position encoding and multi-dimensional feature vectors. The decoder
incorporates four methods: spatial clustering and semantic filtering of
multi-view targets, dynamic matching of multi-dimensional features, space-time
logic-based multi-target tracking, and space-time convergence network
(STCN)-based parameter passing. Through the fusion of multiple decoding
methods, muti-camera targets are tracked in three dimensions: temporal logic,
spatial logic, and feature matching. For the MOT17 dataset, this study's method
significantly outperforms the current state-of-the-art method MiniTrackV2 [49]
by 2.2% to 0.836 on Multiple Object Tracking Accuracy(MOTA) metric.
Furthermore, this study proposes a retrospective mechanism for the first time,
and adopts a reverse-order processing method to optimise the historical
mislabeled targets for improving the Identification F1-score(IDF1). For the
self-built dataset OVIT-MOT01, the IDF1 improves from 0.948 to 0.967, and the
Multi-camera Tracking Accuracy(MCTA) improves from 0.878 to 0.909, which
significantly improves the continuous tracking accuracy and scene adaptation.
This research method introduces a new attentional tracking paradigm which is
able to achieve state-of-the-art performance on multi-target tracking (MOT17
and OVIT-MOT01) tasks.
