Multiple Object Tracking is the problem of automatically identifying multiple objects in a video and representing them as a set of trajectories with high accuracy.
Source: SOT for MOT
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Compared to manual blood cell counting, CycleTrack achieves 96. 58 $\pm$ 2. 43% cell counting accuracy among 8 test videos with 1000 frames each compared to 93. 45% and 77. 02% accuracy for independent CenterTrack and SORT almost without additional time expense.
Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years.
We present MOTChallenge, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data, and create a framework for the standardized evaluation of multiple object tracking methods.
Multiple Object Tracking (MOT) detects the trajectories of multiple objects given an input video, and it has become more and more popular in various research and industry areas, such as cell tracking for biomedical research and human tracking in video surveillance.
Multiple Object Tracking (MOT) is an important task in computer vision.
With the recent advances in the object detection research field, tracking-by-detection has become the leading paradigm adopted by multi-object tracking algorithms.
Meanwhile, the spatial attention, which focuses on the foreground within the bounding boxes, is generated from the given instance masks and applied to the extracted embedding features.
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.