norfair
UNINEXT
norfair | UNINEXT | |
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4 | 2 | |
2,297 | 1,443 | |
1.1% | - | |
7.0 | 5.5 | |
25 days ago | 10 months ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | MIT License |
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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.
UNINEXT
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[R] Universal Instance Perception as Object Discovery and Retrieval (Video Demo)
Hi, we have uploaded the complete paper (https://github.com/MasterBin-IIAU/UNINEXT/blob/master/assets/UNINEXT_Paper.pdf) to our repo. You can find more details in the paper :) About the first question, the input videos are NOT segmented aforehand and all target masks are predicted by our UNINEXT model. For SOT and VOS, we use target annotations (box or mask) from the first frame as the prompts. This helps UNINEXT to segment corresponding targets in the following frames.
What are some alternatives?
multi-object-tracker - Multi-object trackers in Python
VolleyVision - Applying Deep Learning Approaches to Volleyball Data
yolov4-deepsort - Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
ailia-models - The collection of pre-trained, state-of-the-art AI models for ailia SDK
zero-shot-object-tracking - Object tracking implemented with the Roboflow Inference API, DeepSort, and OpenAI CLIP.
py-motmetrics - :bar_chart: Benchmark multiple object trackers (MOT) in Python
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.
TagMaps - Spatio-Temporal Tag and Photo Location Clustering for generating Tag Maps
kalmanpy - Implementation of Kalman Filter in Python
unimatch - [TPAMI'23] Unifying Flow, Stereo and Depth Estimation
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
VNext - Next-generation Video instance recognition framework on top of Detectron2 which supports InstMove (CVPR 2023), SeqFormer(ECCV Oral), and IDOL(ECCV Oral))