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Cross-view multi-object tracking aims to link objects between frames andcamera views with substantial overlaps. Although cross-view multi-objecttracking has received increased attention in recent years, existing datasetsstill have several issues, including 1) missing real-world scenarios, 2)lacking diverse scenes, 3) owning a limited number of tracks, 4) comprisingonly static cameras, and 5) lacking standard benchmarks, which hinder theinvestigation and comparison of cross-view tracking methods. To solve theaforementioned issues, we introduce DIVOTrack: a new cross-view multi-objecttracking dataset for DIVerse Open scenes with dense tracking pedestrians inrealistic and non-experimental environments. Our DIVOTrack has fifteen distinctscenarios and 953 cross-view tracks, surpassing all cross-view multi-objecttracking datasets currently available. Furthermore, we provide a novel baselinecross-view tracking method with a unified joint detection and cross-viewtracking framework named CrossMOT, which learns object detection, single-viewassociation, and cross-view matching with an all-in-one embedding model.Finally, we present a summary of current methodologies and a set of standardbenchmarks with our DIVOTrack to provide a fair comparison and conduct acomprehensive analysis of current approaches and our proposed CrossMOT. Thedataset and code are available at https://github.com/shengyuhao/DIVOTrack.

DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes