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deep_sort
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sort | deep_sort | |
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8 | 10 | |
3,702 | 5,030 | |
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3.3 | 0.0 | |
5 months ago | about 2 months ago | |
Python | Python | |
GNU General Public License v3.0 only | 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.
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- [D] how does military auto turrets like CWIS not get confused when there are multiple targets?
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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.
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How to improve a YoloV5 model after the first training?
One interesting thing to consider is about using a new model's output as training data is the following. If you use some object tracking algorithm like SORT, you might be able to find frames where the object was "tracked" even though the model didn't detect the object (some type of flickering). In this case, you're able to run the model on video and make it better by giving it these new bounding boxes based on "tracking". Does that make sense?
- Temporal filtering of CNN detectors
- How to track an object between detections with a kalman filter?
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[P] Gait Recognition in the wild
The model is an ST-GCN trained in a completely unsupervised manner, from a LOT of skeleton sequences. Pose estimation was performed with AlphaPose, and tracked using SORT.
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Is it possible to track objects on the go?
Yes it is possible! It's an addition to object detection, so I suggest you look for "object detection tracking". You can find articles like this one which refers to Python libraries like this one.
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I made a traffic light detection program with a self-trained dataset
congratz! that's a great project. since you have the detector operating on videos, you could add a simple tracking-by-detection method like SORT. https://github.com/abewley/sort#using-sort-in-your-own-project it's really easy to use, and creates temporally constistent tracks by associating detections in successive frames.
deep_sort
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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.
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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?
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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
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DeepSort with PyTorch(support yolo series)
nwojke/deep_sort
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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.
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[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.
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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.
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Could someone suggest a good article that explains the implementation of deep sort algorithm ?
Deep Sort
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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).
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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?
Beginner-Traffic-Light-Detection-OpenCV-YOLOv3 - This is a python program using YOLO and OpenCV to detect traffic lights. Works in The Netherlands, possibly other countries
yolov4-deepsort - Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
Similari - A framework for building high-performance real-time multiple object trackers
Yolo_mark - GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2
mmdetection - OpenMMLab Detection Toolbox and Benchmark
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 ).
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
DeepSORT - support deepsort and bytetrack MOT(Multi-object tracking) using yolov5 with C++
ScaledYOLOv4 - Scaled-YOLOv4: Scaling Cross Stage Partial Network