fast-reid
deep_sort
Our great sponsors
fast-reid | deep_sort | |
---|---|---|
3 | 10 | |
3,267 | 5,030 | |
1.4% | - | |
1.2 | 0.0 | |
4 months ago | about 2 months ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
fast-reid
-
DeepSort with PyTorch(support yolo series)
fast-reid
-
Assign ID and track moving object with optical flow
On failure, you can try using a re-identification methods like FastReid: https://github.com/JDAI-CV/fast-reid in combination with your detector. A good pipeline that combines everything you seem to need is here: https://github.com/GeekAlexis/FastMOT. It uses a combination of Yolov4 (detector) + Kalman filters, Optical flow (tracker) and FastReid (re-identification)
- Safari translation won't work when lang="en" attribute is set!
deep_sort
-
Similari 0.26.2: MOT framework with Python bindings
Similari is a Rust/Python framework aimed at building sophisticated tracking systems. With Similari, you can develop highly efficient parallelized SORT, DeepSORT, and other sophisticated multiple-object tracking engines.
-
How to integrate DeepSORT with YOLOv8
I'm doing a Python personal project where I'm trying to use YOLOv8 and DeepSORT to detect vehicles from a car's dash cam footage. I succeeded in using YOLOv8 to output the correct bounding boxes by processing each camera frame. However, I tried to add on DeepSORT code, but it made the detection accuracy significantly worse. I'm pretty sure I need to train my own "feature extractor" for DeepSORT to create a new .pb file. I got this information from the deep_sort GitHub link: https://github.com/nwojke/deep_sort. I tried to find resources to do this but they are pretty scarce. Has anyone had experience with this problem?
-
Need to download resources for DeepSORT from pan.baidu.com
The feature model well that looks like it is at that domain you mentioned but why not instead of using this repo use the original authors repo
-
DeepSort with PyTorch(support yolo series)
nwojke/deep_sort
-
Kalman filter in Rust runs 120+ times faster than NumPy, SciKit implementation
I was implementing the Kalman filter for bounding boxes during the last two days. As an inspiration source, I looked at the Python3 Kalman filter implementation that is used in the DeepSORT algorithm and uses NumPy and SciKit under the hood, so it's pretty efficient because all the operations are run inside FFI.
-
[P] The easiest way to process and tag video data
There's tons of work out there when it comes to object tracking such as DeepSort. We've worked to build simpler, more efficient solutions in-house though. Then past that, it's a matter of treating everything in the video as an object (including the whole frame), tracking it, and saving it in a no-SQL DB such that it's easy to query in this way.
-
Building an API + query language for rich data like images and video
Right now, the way we're thinking about it is to turn videos into something that works with the structure of a database like MongoDB. Everything in an image or a video is an object (even the frame itself is an object with a large bounding box), and each of these objects has some attributes and can be tracked over time with some form of object tracking. Given that the objects are tracked, they can each basically be returned as a time-series of each of the attributes associated with that object.
-
Could someone suggest a good article that explains the implementation of deep sort algorithm ?
Deep Sort
-
How do I train the DeepSORT tracker for a custom class?
I was wondering if I could use the same annotated data(in YOLO format) for the training of the tracker as well. I took a look at the original repo for DeepSORT, and it does mention the training using cosine metric learning, but I could not seem to understand how to replicate that for my own dataset(they show us how to do it for the MARS and Market1501 datasets).
-
Is it possible to track objects on the go?
DeepSORT is one of the best trackers https://github.com/nwojke/deep_sortIt requires an object detector tho, like YOLO https://pjreddie.com/darknet/yolo/
What are some alternatives?
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
sort - Simple, online, and realtime tracking of multiple objects in a video sequence.
l2rpn-baselines - L2RPN Baselines a repository to host baselines for l2rpn competitions.
yolov4-deepsort - Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
Similari - A framework for building high-performance real-time multiple object trackers
DOLG-pytorch - Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"
mmdetection - OpenMMLab Detection Toolbox and Benchmark
apex-configs-by-deafps - Apex config & tweaks
yolo_series_deepsort_pytorch - Deepsort with yolo series. This project support the existing yolo detection model algorithm (YOLOV8, YOLOV7, YOLOV6, YOLOV5, YOLOV4Scaled, YOLOV4, YOLOv3', PPYOLOE, YOLOR, YOLOX ).
Hekate-Toolbox - A toolbox for Hekate
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite