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These examples track targets in an area using recorded videos from a camera.
Read camera image sequences and import ground truth and detections in MOT Challenge format.
Implement the Simple Online and Realtime (SORT) object tracking algorithm.
Integrate appearance features from a re-Identification (Re-ID) Deep Neural Network with a multi-object tracker to improve the performance of camera-based object tracking. The implementation closely follows the Deep Simple Online and Realtime (DeepSORT) multi-object tracking algorithm [1]. This example uses the Sensor Fusion and Tracking Toolbox™ and the Computer Vision Toolbox™.
Resolve challenging tracking scenarios when objects are occluded and in close proximity.
Track people throughout a video sequence using re-identification with a residual network.
Create an automation algorithm to automatically label data for object tracking and for object re-identification.
Convert a groundTruth object to the trackCLEARmetrics truth format.
Convert a groundTruth object to the re-identification training data format.
Detect multiple people, track them, and estimate their body poses in a video by using pretrained deep learning networks and a global nearest-neighbor (GNN) assignment tracking approach.
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