GNMOT | Graph Networks for Multiple Object Tracking

by yinizhizhu Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | GNMOT Summary

GNMOT is a Python library. GNMOT has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

This is the official code of 'Graph Networks for Multiple object Tracking'. Multiple object tracking (MOT) task requires reasoning the states of all targets and associating these targets in a global way. However, existing MOT methods mostly focus on the local relationship among objects and ignore the global relationship. Some methods formulate the MOT problem as a graph optimization problem. However, these methods are based on static graphs, which are seldom updated. To solve these problems, we design a new near-online MOT method with an end-to-end graph network. Specifically, we design an appearance graph network and a motion graph network to capture the appearance and the motion similarity separately. The updating mechanism is carefully designed in our graph network, which means that nodes, edges and the global variable in the graph can be updated. The global variable can capture the global relationship to help tracking. Finally, a strategy to handle missing detections is proposed to remedy the defect of the detectors. Our method is evaluated on both the MOT16 and the MOT17 benchmarks, and experimental results show the encouraging performance of our method.

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GNMOT has a low active ecosystem.

It has 31 star(s) with 9 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 3 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of GNMOT is current.

Quality

GNMOT has 0 bugs and 0 code smells.

Security

GNMOT has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

GNMOT code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

GNMOT does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

GNMOT releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

GNMOT saves you 1981 person hours of effort in developing the same functionality from scratch.

It has 4359 lines of code, 273 functions and 24 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed GNMOT and discovered the below as its top functions. This is intended to give you an instant insight into GNMOT implemented functionality, and help decide if they suit your requirements.

Returns a list of matrices
Compute the cost matrix
Make matrix
Pad a matrix
Update the network with the given sequence name
Update UVE
Update the network
Saves the model
Load next step
Resize image
Compute MNM
Generate a list of features
Read the bbx file
Adjusts the bounding box
Generate a random bbx
Makes the cost matrix
Make a cost matrix from a profit matrix
Get motion detection
Updates the position of the motion at a specified location
Swap FC
Calculate the velocity

Get all kandi verified functions for this library.

GNMOT Key Features

No Key Features are available at this moment for GNMOT.

GNMOT Examples and Code Snippets

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Community Discussions

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Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install GNMOT

Install pytorch >= 0.4 following official instruction. Clone this repo, and we'll call the directory that you cloned as ${GN_ROOT}.
Install pytorch >= 0.4 following official instruction.
Clone this repo, and we'll call the directory that you cloned as ${GN_ROOT}.
Install dependencies: pip install -r requirements.txt
Your directory tree should look like this: ${GN_ROOT} ├── MOT ├── App2 | ├── model ├── Motion1 | ├── model ├── GN ├── output ├── ReadMe.md └── requirements.txt
Download pretrained models from our model zoo(BaiduPan(extract code: um4c)) ${GN_ROOT} `-- App2 `-- model |-- ephi1_13 |-- ephi2_13 |-- u_13 |-- uphi_13 |-- vphi_13 `-- Motion1 `-- model |-- ephi_13 |-- u_13 |-- uphi_13
For MOTChallenge data, please download from MOTChallenge Dataset. Download and extract them under {GN_ROOT}/MOT, and make them look like this: ${GN_ROOT} `-- MOT `-- MOT16 |-- train | |-- MOT16-02 | | |-- seqinfo.ini | | |-- gt | | | |-- gt.txt | | |-- det | | | |-- det.txt | | |-- img1 | | | |-- 000001.jpg | | | |-- 000002.jpg | | | |-- 000003.jpg | |-- ... |-- test | |-- MOT16-01 | | |-- seqinfo.ini | | |-- det | | | |-- det.txt | | |-- img1 | | | |-- 000001.jpg | | | |-- 000002.jpg | | | |-- 000003.jpg | |-- ... `-- MOT17 |-- train | |-- MOT17-02-DPM | | |-- seqinfo.ini | | |-- gt | | | |-- gt.txt | | |-- det | | | |-- det.txt | |-- MOT17-02-FRCNN | | |-- seqinfo.ini | | |-- gt | | | |-- gt.txt | | |-- det | | | |-- det.txt | |-- MOT17-02-SDP | | |-- seqinfo.ini | | |-- gt | | | |-- gt.txt | | |-- det | | | |-- det.txt | |-- MOT17-02-POI | | |-- seqinfo.ini | | |-- gt | | | |-- gt.txt | | |-- det | | | |-- det.txt | |-- ... |-- test | |-- MOT17-01-DPM | | |-- seqinfo.ini | | |-- det | | | |-- det.txt | |-- MOT17-01-FRCNN | | |-- seqinfo.ini | | |-- det | | | |-- det.txt | |-- MOT17-01-SDP | | |-- seqinfo.ini | | |-- det | | | |-- det.txt | |-- MOT17-01-POI | | |-- seqinfo.ini | | |-- det | | | |-- det.txt | |-- ...

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