Object tracking

deep sort
deep sort people tracking, based on Yolo

Kalman
https://github.com/abewley/sort tracks any object with yolo as implemented by Fotache repo(Pytorch)

Pose tracking
https://github.com/NVlabs/Deep_Object_Pose This is the official DOPE ROS package for detection and 6-DoF pose estimation of known objects from an RGB camera. The network has been trained on the following YCB objects: cracker box, sugar box, tomato soup can, mustard bottle, potted meat can, and gelatin box

Combine the 6D object pose estimation code with Chris Annin six axis robot to automate Greenhouse pepper growing.

https://github.com/j96w/6-PACK from "6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints" from paperswithcode.com. https://www.youtube.com/watch?v=8Xb6dazqj10

https://github.com/hughw19/NOCS_CVPR2019 Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation at https://arxiv.org/pdf/1901.02970.pdf The goal of this paper is to estimate the 6D pose anddimensions of unseen object instances in an RGB-D im-age. Contrary to “instance-level” 6D pose estimation tasks,our problem assumes that no exact object CAD models areavailable during either training or testing time.

Time cycle
https://github.com/xiaolonw/TimeCycle We introduce a self-supervised method for learning visual correspondence from unlabeled video. The main idea is to use cycle-consistency in time as free supervisory signal for learning visual representations from scratch. At training time, our model learns a feature map representation to be useful for performing cycle-consistent tracking. At test time, we use the acquired representation to find nearest neighbors across space and time. We demonstrate the generalizability of the representation -- without finetuning -- across a range of visual correspondence tasks, including video object segmentation, keypoint tracking, and optical flow. Our approach outperforms previous self-supervised methods and performs competitively with strongly supervised methods.

filter flow
https://github.com/aimerykong/predictive-filter-flow learning with Predictive Filter Flow (PFF) for various vision tasks. PFF is a framework not only supporting self/fully/un-supervised learning on images and videos, but also providing better interpretability that one is able to track every single pixel's movement and its kernels in constructing the output.

Siamese tracking
https://github.com/AlexeyAB/DaSiamRPN Siamese Networks for Visual Object Tracking won the VOT 2018 challenge.

Goturn tracking
https://github.com/nrupatunga/PY-GOTURN/ python version try on ubuntu 16 or 14 if 18 doesn't work

https://github.com/davheld/GOTURN c++ version.

https://www.learnopencv.com/goturn-deep-learning-based-object-tracking/

https://davheld.github.io/GOTURN/GOTURN.pdf

https://davheld.github.io/

deep lk object tracking

General repos
https://github.com/ido90/AyalonRoad Since the small, crowded cars in the videos were failed to be detected by several out-of-the-box detectors, I manually tagged the vehicles within 15 frames and trained a dedicatedly-designed CNN (in the general spirit of Faster RCNN) consisting of pre-trained Resnet34 layers (chosen with accordance to the desired feature-map cell size and receptive field), location-based network (to incorporate road-map information), and a detection & location head.

Since the low frame-rate could not guarantee intersection between the bounding-boxes of the same vehicle in adjacent frames, I replaced the assignment mechanism of SORT tracker with a location-based probabilistic model implemented through a Kalman filter. The resulted traffic data were transformed from pixels to meters units and organized in both spatial and vehicle-oriented structures. Several research questions were addressed, e.g. regarding the relations between speed/density/flux (the fundamental traffic diagram), daily and temporal patterns and the effects of lane-transitions.

links
Yolo_training

Mdnet tracking

Facial Recognition

Neural papers with code