OpenCV Tracker - person-in-hangang/HanRiver GitHub Wiki

reference https://ehsangazar.com/object-tracking-with-opencv-fd18ccdd7369 https://www.pyimagesearch.com/2018/07/30/opencv-object-tracking/

Object Tracking with OpenCV

What is Object Tracking ? Simply put, locating an object in successive frames of a video is called tracking. Usually tracking algorithms are faster than detection algorithms. The reason is simple. When you are tracking an object that was detected in the previous frame, you know a lot about the appearance of the object.

You also know the location in the previous frame and the direction and speed of its motion.

If you are running a face detector on a video and the person’s face get’s occluded by an object, the face detector will most likely fail. A good tracking algorithm, on the other hand, will handle some level of occlusion.

8 OpenCV Object Tracking Implementations

You might be surprised to know that OpenCV includes eight (yes, eight!) separate object tracking implementations that you can use in your own computer vision applications.

1. BOOSTING Tracker: Based on the same algorithm used to power the machine learning behind Haar cascades (AdaBoost), but like Haar cascades, is over a decade old. This tracker is slow and doesn’t work very well. Interesting only for legacy reasons and comparing other algorithms. (minimum OpenCV 3.0.0)

2. MIL Tracker: Better accuracy than BOOSTING tracker but does a poor job of reporting failure. (minimum OpenCV 3.0.0)

3. KCF Tracker: Kernelized Correlation Filters. Faster than BOOSTING and MIL. Similar to MIL and KCF, does not handle full occlusion well. (minimum OpenCV 3.1.0)

4. CSRT Tracker: Discriminative Correlation Filter (with Channel and Spatial Reliability). Tends to be more accurate than KCF but slightly slower. (minimum OpenCV 3.4.2)

5. MedianFlow Tracker: Does a nice job reporting failures; however, if there is too large of a jump in motion, such as fast moving objects, or objects that change quickly in their appearance, the model will fail. (minimum OpenCV 3.0.0)

6. TLD Tracker: I’m not sure if there is a problem with the OpenCV implementation of the TLD tracker or the actual algorithm itself, but the TLD tracker was incredibly prone to false-positives. I do not recommend using this OpenCV object tracker. (minimum OpenCV 3.0.0)

7. MOSSE Tracker: Very, very fast. Not as accurate as CSRT or KCF but a good choice if you need pure speed. (minimum OpenCV 3.4.1)

8. GOTURN Tracker: The only deep learning-based object detector included in OpenCV. It requires additional model files to run (will not be covered in this post). My initial experiments showed it was a bit of a pain to use even though it reportedly handles viewing changes well (my initial experiments didn’t confirm this though). I’ll try to cover it in a future post, but in the meantime, take a look at Satya’s writeup. (minimum OpenCV 3.2.0)

What are the OpenCV Tracker Algorithms - CSRT tracker

We use CSRT tracker.

In the Discriminative Correlation Filter with Channel and Spatial Reliability (DCF-CSR), we use the spatial reliability map for adjusting the filter support to the part of the selected region from the frame for tracking. This ensures enlarging and localization of the selected region and improved tracking of the non-rectangular regions or objects. It uses only 2 standard features (HoGs and Colornames). It also operates at a comparatively lower fps (25 fps) but gives higher accuracy for object tracking. Higher accuracy, a little bit better speed is what CSRT gives us.

How to load in Android

In openCVLibrary - java - o.o.tracking

Load tracker in package com.p4f.objecttracking

In Android

   mTracker = TrackerCSRT.create(); 
                mTracker.init(mImageGrabInit, mInitRectangle); //initalize tracker
                first=false;