City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones Code

Overview

City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

$ pip install -r requirements.txt

Data Preparation

If you want to reproduce our results on AI City Challengef, please download the datasets from: (https://www.aicitychallenge.org/) and put it under the folder datasets. Make sure the data structure is like:

AIC21-MTMC

  • datasets
    • AIC21_Track3_MTMC_Tracking
      • unzip AIC21_Track3_MTMC_Tracking.zip
    • detect_provided (Including detection and corresponding Re-ID features)
  • detector
    • yolov5
  • reid
    • reid_model (Pre-trained reid model on Track 2)
      • resnet101_ibn_a_2.pth
      • resnet101_ibn_a_3.pth
      • resnext101_ibn_a_2.pth

Reproduce frome detect_provided

If you just want reproduce our results, you can directly download detect_provided:

cd AIC21-MTMC
mkdir datasets
cd datasets

Then put detect_provided folder under this folder and modify yml config/aic_mcmt.yml:

CHALLENGE_DATA_DIR: '/home/xxx/AIC21-MTMC/datasets/AIC21_Track3_MTMC_Tracking/'
DET_SOURCE_DIR: '/home/xxx/AIC21-MTMC/datasets/detection/images/test/S06/'
DATA_DIR: '/home/xxx/AIC21-MTMC/datasets/detect_provided'
REID_SIZE_TEST: [384, 384]
ROI_DIR: '/home/xxx/AIC21-MTMC/datasets/AIC21_Track3_MTMC_Tracking/test/S06/'
CID_BIAS_DIR: '/home/xxx/AIC21-MTMC/datasets/AIC21_Track3_MTMC_Tracking/cam_timestamp/'
USE_RERANK: True
USE_FF: True
SCORE_THR: 0.1
MCMT_OUTPUT_TXT: 'track3.txt'

Then run:

bash ./run_mcmt.sh

The final results will locate at path ./reid/reid-matching/tools/track3.txt

Reproduce on all pipeline

If you just want reproduce our results on all pipeline, you have to download:

detector/yolov5/yolov5x.pt
reid/reid_model/resnet101_ibn_a_2.pth
reid/reid_model/resnet101_ibn_a_3.pth
reid/reid_model/resnext101_ibn_a_2.pth

You can refer to Track2 to retrain the reid model.

Then modify yml:

config/aic_all.yml
config/aic_reid1.yml
config/aic_reid2.yml
config/aic_reid3.yml

Then run:

bash ./run_all.sh

The final results will locate at path ./reid/reid-matching/tools/track3.txt

Source code for Acorn, the precision farming rover by Twisted Fields

Acorn precision farming rover This is the software repository for Acorn, the precision farming rover by Twisted Fields. For more information see twist

Twisted Fields 198 Jan 02, 2023
Random Forests for Regression with Missing Entries

Random Forests for Regression with Missing Entries These are specific codes used in the article: On the Consistency of a Random Forest Algorithm in th

Irving Gómez-Méndez 1 Nov 15, 2021
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

Computational Linguistics Research Group 8.4k Jan 03, 2023
Pytorch implementation of "A simple neural network module for relational reasoning" (Relational Networks)

Pytorch implementation of Relational Networks - A simple neural network module for relational reasoning Implemented & tested on Sort-of-CLEVR task. So

Kim Heecheol 800 Dec 05, 2022
Collection of NLP model explanations and accompanying analysis tools

Thermostat is a large collection of NLP model explanations and accompanying analysis tools. Combines explainability methods from the captum library wi

126 Nov 22, 2022
The code for paper Efficiently Solve the Max-cut Problem via a Quantum Qubit Rotation Algorithm

Quantum Qubit Rotation Algorithm Single qubit rotation gates $$ U(\Theta)=\bigotimes_{i=1}^n R_x (\phi_i) $$ QQRA for the max-cut problem This code wa

SheffieldWang 0 Oct 18, 2021
Object Database for Super Mario Galaxy 1/2.

Super Mario Galaxy Object Database Welcome to the public object database for Super Mario Galaxy and Super Mario Galaxy 2. Here, we document all object

Aurum 9 Dec 04, 2022
Joint detection and tracking model named DEFT, or ``Detection Embeddings for Tracking.

DEFT: Detection Embeddings for Tracking DEFT: Detection Embeddings for Tracking, Mohamed Chaabane, Peter Zhang, J. Ross Beveridge, Stephen O'Hara

Mohamed Chaabane 253 Dec 18, 2022
An implementation for the ICCV 2021 paper Deep Permutation Equivariant Structure from Motion.

Deep Permutation Equivariant Structure from Motion Paper | Poster This repository contains an implementation for the ICCV 2021 paper Deep Permutation

72 Dec 27, 2022
Official implementation of Deep Convolutional Dictionary Learning for Image Denoising.

DCDicL for Image Denoising Hongyi Zheng*, Hongwei Yong*, Lei Zhang, "Deep Convolutional Dictionary Learning for Image Denoising," in CVPR 2021. (* Equ

Z80 91 Dec 21, 2022
Reverse engineer your pytorch vision models, in style

🔍 Rover Reverse engineer your CNNs, in style Rover will help you break down your CNN and visualize the features from within the model. No need to wri

Mayukh Deb 32 Sep 24, 2022
SE3 Pose Interp - Interpolate camera pose or trajectory in SE3, pose interpolation, trajectory interpolation

SE3 Pose Interpolation Pose estimated from SLAM system are always discrete, and

Ran Cheng 4 Dec 15, 2022
Code for "Learning Graph Cellular Automata"

Learning Graph Cellular Automata This code implements the experiments from the NeurIPS 2021 paper: "Learning Graph Cellular Automata" Daniele Grattaro

Daniele Grattarola 37 Oct 26, 2022
Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021)

Flexible Networks for Learning Physical Dynamics of Deformable Objects (2021) By Jinhyung Park, Dohae Lee, In-Kwon Lee from Yonsei University (Seoul,

Jinhyung Park 0 Jan 09, 2022
GLIP: Grounded Language-Image Pre-training

GLIP: Grounded Language-Image Pre-training Updates 12/06/2021: GLIP paper on arxiv https://arxiv.org/abs/2112.03857. Code and Model are under internal

Microsoft 862 Jan 01, 2023
Heterogeneous Deep Graph Infomax

Heterogeneous-Deep-Graph-Infomax Parameter Setting: HDGI-A: Node-level dimension: 16 Attention head: 4 Semantic-level attention vector: 8 learning rat

52 Oct 31, 2022
Code release for NeRF (Neural Radiance Fields)

NeRF: Neural Radiance Fields Project Page | Video | Paper | Data Tensorflow implementation of optimizing a neural representation for a single scene an

6.5k Jan 01, 2023
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning

Mammoth - An Extendible (General) Continual Learning Framework for Pytorch NEWS STAY TUNED: We are working on an update of this repository to include

AImageLab 277 Dec 28, 2022
A small fun project using python OpenCV, mediapipe, and pydirectinput

Here I tried a small fun project using python OpenCV, mediapipe, and pydirectinput. Here we can control moves car game when yellow color come to right box (press key 'd') left box (press key 'a') lef

Sameh Elisha 3 Nov 17, 2022
🗺 General purpose U-Network implemented in Keras for image segmentation

TF-Unet General purpose U-Network implemented in Keras for image segmentation Getting started • Training • Evaluation Getting started Looking for Jupy

Or Fleisher 2 Aug 31, 2022