ZHANGHeng19931123 / Awesome Video Object Detection
This is a list of awesome articles about object detection from video.
Stars: ✭ 190
Projects that are alternatives of or similar to Awesome Video Object Detection
Bmw Tensorflow Training Gui
This repository allows you to get started with a gui based training a State-of-the-art Deep Learning model with little to no configuration needed! NoCode training with TensorFlow has never been so easy.
Stars: ✭ 736 (+287.37%)
Mutual labels: object-detection, deep-neural-networks
Cnn Paper2
🎨 🎨 深度学习 卷积神经网络教程 :图像识别,目标检测,语义分割,实例分割,人脸识别,神经风格转换,GAN等🎨🎨 https://dataxujing.github.io/CNN-paper2/
Stars: ✭ 77 (-59.47%)
Mutual labels: object-detection, deep-neural-networks
Medicaldetectiontoolkit
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
Stars: ✭ 917 (+382.63%)
Mutual labels: object-detection, deep-neural-networks
Saliency
TensorFlow implementation for SmoothGrad, Grad-CAM, Guided backprop, Integrated Gradients and other saliency techniques
Stars: ✭ 648 (+241.05%)
Mutual labels: object-detection, deep-neural-networks
Deep learning object detection
A paper list of object detection using deep learning.
Stars: ✭ 10,334 (+5338.95%)
Mutual labels: object-detection, deep-neural-networks
Yolo Tf2
yolo(all versions) implementation in keras and tensorflow 2.4
Stars: ✭ 695 (+265.79%)
Mutual labels: object-detection, deep-neural-networks
Channel Pruning
Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)
Stars: ✭ 979 (+415.26%)
Mutual labels: object-detection, deep-neural-networks
Fire Detection Cnn
real-time fire detection in video imagery using a convolutional neural network (deep learning) - from our ICIP 2018 paper (Dunnings / Breckon) + ICMLA 2019 paper (Samarth / Bhowmik / Breckon)
Stars: ✭ 340 (+78.95%)
Mutual labels: object-detection, deep-neural-networks
Ssd Pytorch
SSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity
Stars: ✭ 107 (-43.68%)
Mutual labels: object-detection, deep-neural-networks
Opentpod
Open Toolkit for Painless Object Detection
Stars: ✭ 106 (-44.21%)
Mutual labels: object-detection, deep-neural-networks
Vehicle counting tensorflow
🚘 "MORE THAN VEHICLE COUNTING!" This project provides prediction for speed, color and size of the vehicles with TensorFlow Object Counting API.
Stars: ✭ 582 (+206.32%)
Mutual labels: object-detection, deep-neural-networks
Unsupervised detection
An Unsupervised Learning Framework for Moving Object Detection From Videos
Stars: ✭ 139 (-26.84%)
Mutual labels: object-detection, deep-neural-networks
Tracking With Darkflow
Real-time people Multitracker using YOLO v2 and deep_sort with tensorflow
Stars: ✭ 515 (+171.05%)
Mutual labels: object-detection, deep-neural-networks
Bmw Yolov4 Inference Api Cpu
This is a repository for an nocode object detection inference API using the Yolov4 and Yolov3 Opencv.
Stars: ✭ 180 (-5.26%)
Mutual labels: object-detection, deep-neural-networks
Yolo2 Pytorch
PyTorch implementation of the YOLO (You Only Look Once) v2
Stars: ✭ 426 (+124.21%)
Mutual labels: object-detection, deep-neural-networks
Tensorflow object counting api
🚀 The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems!
Stars: ✭ 956 (+403.16%)
Mutual labels: object-detection, deep-neural-networks
Bmw Tensorflow Inference Api Gpu
This is a repository for an object detection inference API using the Tensorflow framework.
Stars: ✭ 277 (+45.79%)
Mutual labels: object-detection, deep-neural-networks
Yolo V2 Pytorch
YOLO for object detection tasks
Stars: ✭ 302 (+58.95%)
Mutual labels: object-detection, deep-neural-networks
Tensorflow2.0 Examples
🙄 Difficult algorithm, Simple code.
Stars: ✭ 1,397 (+635.26%)
Mutual labels: object-detection, deep-neural-networks
Swa object detection
SWA Object Detection
Stars: ✭ 128 (-32.63%)
Mutual labels: object-detection, deep-neural-networks
Awesome Video-Object-Detection
This is a list of awesome articles about object detection from video.
Datasets
ImageNet VID Challenge
- Site: http://image-net.org/challenges/LSVRC/2017/#vid
- Kagge: https://www.kaggle.com/account/login?returnUrl=%2Fc%2Fimagenet-object-detection-from-video-challenge
VisDrone Challenge
- Site: http://aiskyeye.com/
Paper list
2016
Seq-NMS for Video Object Detection
- Date: Feb 2016
- Motivation: Smoothing the final bounding box predictions across time.
- Summary: Constructing a temporal graph from overlapping bounding box detections across the adjacent frames, and using dynamic programming to select bounding box sequences with the highest overall detection score.
T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
- Date: Apr 2016
- Summary: Using a video object detection pipeline that involves predicting optical flow first, then propagating image level predictions according to the flow, and finally using a tracking algorithm to select temporally consistent high confidence detections.
- Performance: 73.8% mAP on ImageNet VID validation.
Object Detection from Video Tubelets with Convolutional Neural Networks
- Date: Apr 2016
Deep Feature Flow for Video Recognition
- Date: Nov 2016
- Performance: 73.0% mAP on ImageNet VID validation at 29 fps on a Titan X GPU.
2017
Object Detection in Videos with Tubelet Proposal Networks
- Date: Feb 2017
Flow-Guided Feature Aggregation for Video Object Detection
- Date: Mar 2017
- Motivation: Producing powerful spatiotemporal features.
- Performance: 76.3% mAP at 1.4 fps or 78.4% (combined with Seq-NMS) at 1.1 fps on ImageNet VID validation on a Titan X GPU.
Detect to Track and Track to Detect
- Date: Oct 2017
- Motivation: Smoothing the final bounding box predictions across time.
- Summary: Proposing a ConvNet architecture that solves detection and tracking problems jointly and applying a Viterbi algorithm to link the detections across time.
- Performance: 79.8% mAP on ImageNet VID validation.
Towards High Performance Video Object Detection
- Date: Nov 2017
- Motivation: Producing powerful spatiotemporal features.
- Performance: 78.6% mAP on ImageNet VID validation at 13 fps on a Titan X GPU.
Video Object Detection with an Aligned Spatial-Temporal Memory
[Arxiv] [Summary] [Code] [Demo]
- Date: Dec 2017
- Motivation: Producing powerful spatiotemporal features.
- Performance: 80.5% mAP on ImageNet VID validation.
2018
Object Detection in Videos by High Quality Object Linking
- Date: Jan 2018
Towards High Performance Video Object Detection for Mobiles
- Date: Apr 2018
- Motivation: Producing powerful spatiotemporal features.
- Performance: 60.2% mAP on ImageNet VID validation at 25.6 fps on mobiles.
Optimizing Video Object Detection via a Scale-Time Lattice
- Date: Apr 2018
- Performance: 79.4% mAP at 20 fps or 79.0% at 62 fps on ImageNet VID validation on a Titan X GPU.
Object Detection in Video with Spatiotemporal Sampling Networks
- Date: Mar 2018
- Motivation: Producing powerful spatiotemporal features.
- Performance: 78.9% mAP or 80.4% (combined with Seq-NMS) on ImageNet VID validation.
Fully Motion-Aware Network for Video Object Detection
- Date: Stp. 2018
- Motivation: Producing powerful spatiotemporal features.
- Performance: 78.1% mAP or 80.3% (combined with Seq-NMS) on ImageNet VID validation.
Integrated Object Detection and Tracking with Tracklet-Conditioned Detection
- Date: Nov 2018
- Motivation: Smoothing the final bounding box predictions across time.
- Performance: 83.5% of mAP with FGFA and Deformable ConvNets v2 on ImageNet VID validation.
2019
AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
- Date: Feb 2019
- Motivation: Adaptively rescale the input image resolution to improve both accuracy and speed for video object detection.
- Performance: 75.5% of mAP on ImageNet VID validation for 4 different multi-scale training (600, 480, 360, 240).
Improving Video Object Detection by Seq-Bbox Matching
- Date: Feb 2019
- Motivation: Smoothing the final bounding box predictions across time (box-level method).
- Performance: 80.9% of mAP (offline detection) and 78.2% of mAP (online detection) both at 38 fps on a Titan X GPU.
Comparison table
Paper | Date | Base detector | Backbone | Tracking? | Optical flow? | Online? | mAP(%) | FPS (Titan X) |
---|---|---|---|---|---|---|---|---|
Seq-NMS | Feb 2016 | R-FCN | ResNet101 | no | no | no | 76.8 | 2.3 |
T-CNN | Apr 2016 | RCNN | DeepIDNet+CRAFT | yes | no | no | 73.8 | - |
DFF | Nov 2016 | R-FCN | ResNet101 | no | yes | yes | 73.0 | 29 |
TPN | Feb 2017 | TPN | GoogLeNet | yes | no | no | 68.4 | - |
FGFA | Mar 2017 | R-FCN | ResNet101 | no | yes | yes | 76.3 | 1.4 |
FGFA + Seq-NMS | 29 Mar 2017 | R-FCN | ResNet101 | no | yes | no | 78.4 | 1.14 |
D&T | Oct 2017 | R-FCN (15 anchors) | ResNet101 | yes | no | no | 79.8 | 7.09 |
STMN | Dec 2017 | R-FCN | ResNet101 | no | no | no | 80.5 | - |
Scale-time-lattice | 16 Apr 2018 | Faster RCNN (15 anchors) | ResNet101 | no | no | no | 79.6 | 20 |
Scale-time-lattice | Apr 2018 | Faster RCNN (15 anchors) | ResNet101 | no | no | no | 79.0 | 62 |
SSN (per-frame baseline for STSN) | Mar 2018 | R-FCN | Deformable ResNet101 | no | no | yes | 76.0 | - |
STSN | Mar 2018 | R-FCN | Deformable ResNet101 | no | no | yes | 78.9 | - |
STSN+Seq-NMS | Mar 2018 | R-FCN | Deformable ResNet101 | no | no | no | 80.4 | - |
MANet | Sep. 2018 | R-FCN | ResNet101 | no | yes | yes | 78.1 | 5 |
MANet+Seq-NMS | Sep. 2018 | R-FCN | ResNet101 | no | yes | no | 80.3 | - |
Tracklet-Conditioned Detection | Nov 2018 | R-FCN | ResNet101 | yes | no | yes | 78.1 | - |
Tracklet-Conditioned Detection+DCNv2 | Nov 2018 | R-FCN | ResNet101 | yes | no | yes | 82.0 | - |
Tracklet-Conditioned Detection+DCNv2+FGFA | Nov 2018 | R-FCN | ResNet101 | yes | no | yes | 83.5 | - |
Seq-Bbox Matching | Feb 2019 | YOLOv3 | darknet53 | no | no | no | 80.9 | 38 |
Seq-Bbox Matching | Feb 2019 | YOLOv3 | darknet53 | no | no | yes | 78.2 | 38 |
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].