The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking
This provides a benchmark for developing accurate vehicle trajectory extraction algorithms, which is important for traffic monitoring and understanding autonomous vehicle impacts, but it is incremental as it builds on existing datasets and methods.
They tackled the problem of 3D multi-camera vehicle tracking by introducing a new video dataset from overlapping highway cameras, resulting in 877,000 annotated 3D bounding boxes and tracklets across 57 minutes of footage.
This work presents a novel video dataset recorded from overlapping highway traffic cameras along an urban interstate, enabling multi-camera 3D object tracking in a traffic monitoring context. Data is released from 3 scenes containing video from at least 16 cameras each, totaling 57 minutes in length. 877,000 3D bounding boxes and corresponding object tracklets are fully and accurately annotated for each camera field of view and are combined into a spatially and temporally continuous set of vehicle trajectories for each scene. Lastly, existing algorithms are combined to benchmark a number of 3D multi-camera tracking pipelines on the dataset, with results indicating that the dataset is challenging due to the difficulty of matching objects traveling at high speeds across cameras and heavy object occlusion, potentially for hundreds of frames, during congested traffic. This work aims to enable the development of accurate and automatic vehicle trajectory extraction algorithms, which will play a vital role in understanding impacts of autonomous vehicle technologies on the safety and efficiency of traffic.