Deepsort with Torchvision detectors
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DeepSORT based Tracking is based on the following steps: Detection: Before objects can be tracked in each frame, they must be detected. This is done using a standard object detector like Faster R-CNN. Feature Extraction: Extract features from the detected objects. These features will help to match objects across frames. Data Association: Match detected objects with tracked objects from previous frames using both the bounding box overlap (using the Hungarian algorithm) and the feature similarity. Track Management: Update tracks or create/delete tracks as necessary.
Tasks: Object Detection, Feature Extraction, Multimodal
Task Categories: Computer Vision
Published: 09/29/23
Tags
pytorch
DeepSORT
inference-ready
tracking
Faster R-CNN
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