HHCL-ReID
norfair
HHCL-ReID | norfair | |
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1 | 4 | |
133 | 2,303 | |
- | 1.4% | |
0.0 | 7.0 | |
almost 2 years ago | about 1 month ago | |
Python | Python | |
- | BSD 3-clause "New" or "Revised" License |
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HHCL-ReID
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Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification
Unsupervised person re-identification (Re-ID) is a promising and very challenging research problem in computer vision. Learning robust and discriminative features with unlabeled data is of central importance to Re-ID. Recently, more attention has been paid to unsupervised Re-ID algorithms based on clustered pseudo-label. However, the previous approaches did not fully exploit information of hard samples, simply using cluster centroid or all instances for contrastive learning. In this paper, we propose a Hard-sample Guided Hybrid Contrast Learning (HHCL) approach combining cluster-level loss with instance-level loss for unsupervised person Re-ID. Our approach applies cluster centroid contrastive loss to ensure that the network is updated in a more stable way. Meanwhile, introduction of a hard instance contrastive loss further mines the discriminative information. Extensive experiments on two popular large-scale Re-ID benchmarks demonstrate that our HHCL outperforms previous state-of-the-art methods and significantly improves the performance of unsupervised person Re-ID. The code of our work is available soon at https://github.com/bupt-ai-cz/HHCL-ReID.
norfair
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Seeking Efficient Video Object Tracking and or Video Segmentation Software for Research
If you're familiar with Python you can try using Norfair. It's a lightweight Python library for adding real-time multi-object tracking to any detector. There are lots of examples for you to try, they mostly differ on the object detector.
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Help combining custom detector (yolo) with a tracker.
Yyou can use norfair: https://github.com/tryolabs/norfair
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Empresa uruguaya que SI recomendarías
Tryolabs
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Tracker on top of YOLO algorithm?
Depending on your task normal SORT would work as well (No retraining required, just some Kallman filter and some other techniques). The library https://github.com/tryolabs/norfair has implemented SORT and can be extended with DeepSORT if you want.
What are some alternatives?
multi-object-tracker - Multi-object trackers in Python
yolov4-deepsort - Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
zero-shot-object-tracking - Object tracking implemented with the Roboflow Inference API, DeepSort, and OpenAI CLIP.
mmtracking - OpenMMLab Video Perception Toolbox. It supports Video Object Detection (VID), Multiple Object Tracking (MOT), Single Object Tracking (SOT), Video Instance Segmentation (VIS) with a unified framework.
kalmanpy - Implementation of Kalman Filter in Python
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
Kornia - Geometric Computer Vision Library for Spatial AI
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
UNINEXT - [CVPR'23] Universal Instance Perception as Object Discovery and Retrieval
PeekingDuck - A modular framework built to simplify Computer Vision inference workloads.