Kalman tracking

https://github.com/m-dorgham/PedestrianTracking 1. Humans detection
I implemented two approaches for detecting people, the first is by using background subtraction and supporting it by a neural network trained to classify humans, I retrained Inception v3 model via tensorflow to classify humans vs. non-humans (the model is included in the project).
The second detection method is Yolo which gave very good detection results.
2. Human tracking
I used Unscented Kalman filter to keep track of the dynamics of the motion of each detected human, and used the Hungarian algorithm to solve the assignment problem. I used the tracking submodule from Smorodov's Multitarget-tracker but I modified the state change function and the initialization of the initial state. 
https://www.youtube.com/watch?v=v2D3t0t7gWM dataset: Kinect Tracking Precision (KTP) dataset. Detection: Using Yolo. Tracking: Unscented kalman filter with the Hungarian algorithm.
=== multitarget === 
https://github.com/Smorodov/Multitarget-tracker Background substraction: built-in Vibe, SuBSENSE and LOBSTER; MOG2 from opencv; MOG, GMG and CNT from opencv_contrib
Foreground segmentation: contours
Matching: Hungrian algorithm or algorithm based on weighted bipartite graphs
Tracking: Linear or Unscented Kalman filter for objects center or for object coordinates and size
Use or not local tracker (LK optical flow) for smooth trajectories
KCF, MIL, MedianFlow, GOTURN or MOSSE tracking for lost objects and collision resolving
Haar face detector from OpenCV
HOG and C4 pedestrian detectors
SSD detector from OpenCV and models from chuanqi305/MobileNet-SSD
YOLO and Tiny YOLO detectors from https://pjreddie.com/darknet/yolo/
 
=== links ===
KalmanFilter
Yolo
SORT tracking
Category:Yolo
Category:Object tracking
Category:Github
Category:Math
Category:Neural networks
Category:Kalman