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Most modern Multi-Object Tracking (MOT) systems typically apply REID-based paradigm to hold a balance between computational efficiency and performance.
Some challenging problems in tracking multiple objects include the time-dependent cardinality, unordered measurements and object parameter labeling.
One of the core components in online multiple object tracking (MOT) frameworks is associating new detections with existing tracklets, typically done via a scoring function.
In this paper, we propose a multiple object tracker, called MF-Tracker, that integrates multiple classical features (spatial distances and colours) and modern features (detection labels and re-identification features) in its tracking framework.
The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods.
Recent works in multiple object tracking use sequence model to calculate the similarity score between the detections and the previous tracklets.
Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without superivison.
Graphs offer a natural way to formulate Multiple Object Tracking (MOT) within the tracking-by-detection paradigm.