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Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms.
In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.
SOTA for Multi-Object Tracking on MOT16 (using extra training data)
In this paper, we harness the power of deep learning for data association in tracking by jointly modelling object appearances and their affinities between different frames in an end-to-end fashion.
The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform.
In this project, we implement a multiple object tracker, following the tracking-by-detection paradigm, as an extension of an existing method.
To directly optimize a tracker based on MOTA and MOTP is difficult, since both the metrics are strongly rely on the Hungarian algorithm, which are non-differentiable.
Recently, a new benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal of collecting existing and new data and creating a framework for the standardized evaluation of multiple object tracking methods.
In this work, we present an end-to-end framework to settle data association in online Multiple-Object Tracking (MOT).