UnsMOT: Unified Framework for Unsupervised Multi-Object Tracking with
  Geometric Topology Guidance
Son Tran Cong Tran Anh Tran Cuong Pham
Object detection has long been a topic of high interest in computer vision
literature. Motivated by the fact that annotating data for the multi-object
tracking (MOT) problem is immensely expensive, recent studies have turned their
attention to the unsupervised learning setting. In this paper, we push forward
the state-of-the-art performance of unsupervised MOT methods by proposing
UnsMOT, a novel framework that explicitly combines the appearance and motion
features of objects with geometric information to provide more accurate
tracking. Specifically, we first extract the appearance and motion features
using CNN and RNN models, respectively. Then, we construct a graph of objects
based on their relative distances in a frame, which is fed into a GNN model
together with CNN features to output geometric embedding of objects optimized
using an unsupervised loss function. Finally, associations between objects are
found by matching not only similar extracted features but also geometric
embedding of detections and tracklets. Experimental results show remarkable
performance in terms of HOTA, IDF1, and MOTA metrics in comparison with
state-of-the-art methods.