# Introduction to Object Tracking

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## Overview

Object tracking is a computer vision technique that involves tracking an object in a video sequence over time. The main goal of object tracking is to estimate the **position and motion of an object** in a given scene. Object tracking opencv has various applications in video surveillance, traffic monitoring, human-computer interaction, and robotics. Object tracking is different from object detection, which focuses on detecting the presence of objects in a given **image or video frame**.

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## Scope

* This article aims to provide an introduction to **object tracking opencv**, covering its basics, concepts, and techniques.
* We will explore the differences between object tracking and object detection, and examine the various stages and levels of the object tracking process.
* We will then see the process of implementing **object tracking using V7** and also in OpenCV.

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## Introduction

Object tracking opencv is a technique used to monitor and follow objects as they move in a video or a sequence of frames. This technology is used in a wide range of applications, from traffic surveillance to sports analysis to human-robot interaction. Object tracking enables us to locate and track an **object's movement in real-time**, making it an essential tool for tasks that require continuous monitoring and analysis.

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## What is Object Tracking?

Object tracking is the process of locating and following an object's movement in a video or a sequence of frames. The goal of object tracking is to identify an object of interest in the first frame and track it as it moves in subsequent frames. This technique can be used for a variety of applications, including **object recognition, traffic monitoring, and video surveillance**.

Object tracking opencv is a **challenging** task due to various factors such as occlusion, background clutter, illumination changes, and motion blur.

Researchers and engineers have developed various techniques to improve the accuracy and efficiency of object tracking, including traditional approaches like optical flow and **Kalman filtering** and more recent deep learning-based approaches like **YOLOv7** and **Siamese networks**. OpenCV is a widely used library for implementing object tracking algorithms in real-time applications.



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## Object Tracking vs. Object detection

Object tracking and object detection are two related but **distinct computer vision techniques**.

Object detection is the process of identifying and **localizing** objects in an image or a video frame. Object detection techniques can identify **multiple objects** in a single image and provide the location of each object in the image.

On the other hand, object tracking opencv techniques are used to follow an **object's movement across multiple frames**. The table below summarizes the key differences between object tracking and object detection.

| Object Detection | Object Tracking |
|------------------|-------------------|
| Identify objects in a single frame | Follow objects across multiple frames |
| Can detect multiple objects in a single image | Tracks a single object or multiple objects |
| Provides the location of each object in the image | Provides the object's trajectory over time |
| Does not consider object motion | Considers object motion and appearance over time |

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## Different stages of the Object Tracking process

The object tracking opencv process involves several stages, which can be grouped into three main categories: initialization, tracking, and termination.

**Initialization:** In this stage, the object of interest is identified in the first frame of the video or the sequence of frames. This is typically done using object detection techniques.

**Tracking:** Once the object of interest is identified in the first frame, it is tracked in subsequent frames using various tracking techniques. These techniques can be based on the object's appearance, motion, or both.

**Termination:** The object tracking process is terminated when the object of interest is no longer visible or when the tracking algorithm fails to track the object accurately.

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## Levels of Object Tracking

Object tracking can be categorized into three levels based on the level of complexity of the tracking process.

**Point Tracking:** Point tracking involves tracking a single point or feature in an image. This technique is often used for simple applications such as face tracking or motion analysis.


**Object Tracking:** Object tracking opencv involves tracking an object's movement in a video or a sequence of frames. This technique is commonly used in applications such as surveillance and robotics.



**Event Tracking:** Event tracking involves tracking complex events that involve multiple objects, such as traffic monitoring or sports analysis. This technique requires advanced algorithms that can analyze the behavior of multiple objects over time.


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## Challenges in Object tracking

Object tracking opencv is a complex problem and there are several challenges that need to be addressed in order to achieve accurate and robust tracking. Some of the main challenges of object tracking are:

### **Occlusion in object racking opencv:**

When an object is partially or fully occluded, it can be difficult to track it accurately. Occlusion can occur when an object is blocked by another object in the scene or when the object moves behind a foreground object.

One solution to occlusion is to **use multiple object detectors to track different parts of the object** when it is occluded. For example, a face tracker may use multiple detectors to track the face, eyes, nose, and mouth separately. When a part of the face is occluded, the tracker can use the other detectors to estimate the location of the occluded part.

Another approach is to use **object-level occlusion reasoning** to estimate the likelihood that an object is occluded and adjust the tracking accordingly. This involves modeling the occlusion patterns in the scene and using them to predict the likelihood of occlusion at each frame.


### **Illumination changes in object tracking OpenCV:**

Changes in lighting conditions can make it difficult to track an object accurately. For example, shadows or reflections can cause the appearance of an object to change.

One solution to illumination changes is to use **color constancy algorithms** to normalize the color of the object across different lighting conditions. Color constancy algorithms estimate the color of the illumination and use it to adjust the color of the object.

Another approach is to use **multiple image representations** that are robust to changes in lighting conditions. For example, a tracking algorithm may use both color and texture features to track the object. If the color features are affected by illumination changes, the texture features can still provide a reliable tracking signal.


### **Scale changes in object racking opencv:**

When an object changes size, it can be difficult to track it accurately. Scale changes can occur when an object moves closer or further away from the camera.

One solution to scale changes is to use **scale-invariant feature descriptors** that are robust to changes in scale. These feature descriptors can be used to track the object across different scales without requiring explicit scale estimation.

Another approach is to use a **multi-scale tracking framework** that can track an object across different scales. This involves estimating the scale of the object at each frame and using it to adjust the tracking accordingly.


### **Deformation in object tracking opencv:**

When an object changes shape, it can be difficult to track it accurately. This can occur when an object moves in a non-rigid way or when the object itself is deformed.

One solution to deformation is to use **non-rigid registration techniques** to align the object across different frames. Non-rigid registration techniques can estimate the deformation of the object and use it to adjust the tracking accordingly.


Another approach is to use **shape models to estimate the deformation** of the object and adjust the tracking accordingly. This involves building a statistical model of the object's shape and using it to estimate the deformation at each frame.

### **Motion blur in object tracking OpenCV:**

When an object is moving quickly, it can appear blurred in the video frames. This can make it difficult to track the object accurately.

One solution to motion blur is to use **motion deblurring algorithms** to recover the sharp image of the object. Motion deblurring algorithms estimate the motion blur kernel of the image and use it to recover the sharp image.

Another approach is to use **tracking-by-detection techniques** that rely on the appearance of the object rather than its motion. These techniques can be robust to motion blur because they do not rely on the precise motion of the object.


### **Object appearance changes in object tracking OpenCV:**

Changes in an object's appearance, such as changes in color or texture, can make it difficult to track the object accurately.

One solution to appearance changes is to use **adaptive appearance models** that can update the object's appearance over time. Adaptive appearance models can learn the appearance of the object from the tracked frames and use it to update the object model.

Another approach is to use **multi-modal feature representations** that are robust to changes in appearance. For example, a tracking algorithm may use both color and texture features to track the object. If the color of the object changes, the texture features can still provide a reliable tracking signal.


### **Tracking initialization in object tracking opencv:**

Object tracking algorithms often require initialization, which involves selecting the object to be tracked in the first frame of the video. If the object is not selected accurately, the tracking algorithm may fail.

One solution to tracking initialization is to use **interactive methods** that allow the user to select the object to be tracked. Interactive methods can provide accurate initialization because they rely on human perception.

Another approach is to use **online learning techniques** to update the object model as the tracking progresses. Online learning techniques can adjust the object model as the tracking progresses and improve its accuracy.


### **Computational complexity in object tracking opencv:**

Object tracking algorithms can be computationally intensive, which can make real-time tracking challenging.

One solution to computational complexity is to use **efficient algorithms and data structures** that can perform tracking in real-time. Efficient algorithms and data structures can reduce the computational load of the tracking algorithm and make it feasible to perform tracking in real-time.

Another approach is to use **parallel computing techniques** to distribute the computational load across multiple processors or GPUs. Parallel computing techniques can significantly reduce the processing time and make it possible to perform tracking on high-resolution video streams in real-time.


### **Camera motion in object tracking opencv:**

Camera motion can cause the entire scene to move, which can make it difficult to track objects accurately.

One solution to camera motion is to use **motion compensation techniques** to compensate for the camera motion. Motion compensation techniques can estimate the camera motion and use it to compensate for the motion of the object.

Another approach is to use a **multi-camera setup** to track the object from different viewpoints. This involves using multiple cameras to capture the object from different viewpoints and fusing the tracking results to obtain a more robust estimate of the object's position.


### **Tracking in crowded scenes :**

Tracking in crowded scenes is a challenging problem because of the large number of objects that need to be tracked simultaneously.

One solution to tracking in crowded scenes is to use **multi-object tracking algorithms** that can track multiple objects simultaneously. Multi-object tracking algorithms can track the objects and maintain their identities over time.

Another approach is to use **object-level occlusion reasoning** to estimate the likelihood of occlusion and adjust the tracking accordingly. This involves modeling the occlusion patterns in the scene and using them to predict the likelihood of occlusion at each frame. Additionally, incorporating contextual information, such as the scene layout, can also aid in tracking objects in crowded scenes.

Addressing these challenges requires advanced algorithms and techniques, and researchers are continually working on improving object tracking opencv performance.


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## Deep Learning-based approaches to Object Tracking opencv

**Convolutional Neural Networks (CNNs) for feature extraction:**

CNNs are a type of Deep Learning network that is commonly used for image and video processing. CNNs can be used to automatically extract relevant features from the input frames, which can be used to track the object. The features learned by CNNs are robust to changes in lighting, appearance, and background clutter, making them suitable for tracking in challenging environments.


**Siamese Networks for learning a similarity metric between object and background:**

Siamese Networks are another type of Deep Learning network that can be used for object tracking. They learn a similarity metric between the object and the surrounding background, which is used to track the object over time. Siamese Networks are particularly useful when the appearance of the object changes significantly over time.


**Recurrent Neural Networks (RNNs) for modeling temporal dependencies:**

RNNs are a type of Deep Learning network that can model temporal dependencies in the object's motion. RNNs can be used to predict the future trajectory of the object based on its past motion, which can improve the accuracy of the object tracking opencv.


**Siamese-RPN**

Recently, the **combination of the Siamese network and deep regression network** has been proposed, called the **Siamese-RPN (Region Proposal Network)**. This method utilizes the Siamese network to generate proposals and the deep regression network to refine the proposals.


Overall, deep learning-based methods have shown promising results in object tracking, but they also require large amounts of training data and computing resources.

### Visual Object Tracking using V7

Before proceeding, make sure to request a **14-day free trial** if you haven't already. Now, let's briefly go through the steps involved in Visual Object Tracking using V7.

**Upload Data**
The first step is to upload the data.


**Data Annotation**


After uploading your video, the next step is to annotate it. To speed up the process, you can take advantage of V7's auto-annotation tool.

To start annotating, choose the frame where you want to begin tracking the object, which in this example is a runner with a white shirt. You can use the timeline bar located at the bottom of the video to navigate to the desired frame.

After selecting the desired frame, use the auto annotation tool to draw a polygon around the object of interest. It's crucial to select the instance ID as the subtype during annotation. To do so, you must first navigate to the Classes tab in your dataset, create or edit a class, and add Instance ID as a Subtype.


**Instance ID**

Instance ID is a **unique identification number** assigned to a selected object in the video. It helps to keep track of the object throughout the video, even if it changes its orientation or position. The instance ID is useful in classifying and detecting the same object in multiple frames of the video.

**Adding and deleting anchor points**

The **auto annotation tool** generates a segmentation around the selected object, such as the runner in this case. However, you may notice that the segmentation mask is incomplete in certain areas.


To address this, simply hover your mouse over the areas where the segmentation mask is missing and extend it to the desired area within the polygon. You can then click on the areas where the segmentation mask is incomplete to complete the segmentation.


In the image shown above, the **extended segmentation mask** of the runner’s body is done manually by clicking on the areas where it was missing. The green dots indicate the added segmentation, while the red dots represent the deleted ones. After completing the annotation, move to the next frame.

To annotate the object in the next frame, click on the rerun button located on **top of the polygon mask** around the object. This will automatically apply the segmentation mask to the object. You can continue annotating your object in subsequent frames by copying the instances and adjusting the label until you have annotated all instances.

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## Implementation of object tracking OpenCV

Here is the implementation of object tracking opencv using camshift algorithm:

import numpy as np
import cv2 as cv


# Open the input video file
video_capture = cv.VideoCapture('sample.mp4')

# Read the first frame of the video
ret, frame = video_capture.read()

# Set up the initial region of interest (ROI) for object tracking
roi_x, roi_y, roi_width, roi_height = 400, 440, 150, 150
roi_rect = (roi_x, roi_y, roi_width, roi_height)

# Extract the ROI from the first frame and convert it to the HSV color space
roi_frame = frame[roi_y:roi_y+roi_height, roi_x:roi_x+roi_width]
roi_hsv = cv.cvtColor(roi_frame, cv.COLOR_BGR2HSV)

# Create a binary mask for the ROI based on its color range in the HSV color space
roi_mask = cv.inRange(roi_hsv, np.array((0., 60., 32.)), np.array((180., 255., 255.)))

# Compute the histogram of the ROI in the HSV color space
roi_hist = cv.calcHist([roi_hsv], [0], roi_mask, [180], [0, 180])

# Normalize the histogram to a range of 0-255
cv.normalize(roi_hist, roi_hist, 0, 255, cv.NORM_MINMAX)

# Set up the termination criteria for the CamShift algorithm
term_criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 15, 2)

# Start the object tracking loop
while True:
    # Read a frame from the video
    ret, frame = video_capture.read()
    if not ret:
        break
    
    # Resize the frame for better performance
    frame = cv.resize(frame, (720, 720), interpolation=cv.INTER_CUBIC)
    
    # Threshold the frame to remove some noise
    _, frame_thresh = cv.threshold(frame, 180, 155, cv.THRESH_TOZERO_INV)
    
    # Convert the frame to the HSV color space
    frame_hsv = cv.cvtColor(frame_thresh, cv.COLOR_BGR2HSV)

    # Backproject the histogram of the ROI onto the current frame in the HSV color space
    frame_bp = cv.calcBackProject([frame_hsv], [0], roi_hist, [0, 180], 1)
    
    # Apply the CamShift algorithm to the backprojection to get the new location of the object
    _, roi_rect = cv.CamShift(frame_bp, roi_rect, term_criteria)

    # Convert the rotated rectangle returned by CamShift to a polygon and draw it on the frame
    roi_pts = cv.boxPoints(roi_rect)
    roi_pts = np.int0(roi_pts)
    frame_tracking = cv.polylines(frame, [roi_pts], True, (0, 255, 255), 2)

    # Display the frame with the object tracking window overlaid
    cv.imshow('Object Tracking', frame_tracking)

    # Exit the loop if the 'ESC' key is pressed
    key = cv.waitKey(30)
    if key == 27:
        break

# Release the video capture object and close all windows
video_capture.release()
cv.destroyAllWindows()

The code is an implementation of the CamShift algorithm for object tracking opencv in a video. Here is what the code does:

**Imports necessary libraries:** The code imports the NumPy and OpenCV libraries.

**Opens the video:** The code uses OpenCV's VideoCapture() function to read the input video.

**Initializes the tracker:** The code sets up the initial region of interest (ROI) for tracking using the cap.read() function to read the first frame of the video. The ROI is defined by the x, y, width, and height variables, which are used to create a track window.

**Calculates the histogram of the ROI**: The code selects the ROI from the first frame and converts it to the HSV color space. Then it performs masking operation and calculates the histogram of the ROI using the cv.calcHist() function.

**Normalizes the histogram:** The histogram is then normalized using the cv.normalize() function.

**Performs object tracking using CamShift:** The code reads each frame of the video and resizes it. It then applies thresholding and converts the frame from BGR to HSV format. The back projection of the histogram is calculated using cv.calcBackProject(). CamShift algorithm is applied to get the new location of the object in the frame using cv.CamShift(). A box is drawn around the object using the cv.polylines() function.

**Displays the results:** The code displays the original video frame and the CamShift output side-by-side using the cv.imshow() function.

**Exits the loop**: The loop continues until the 'ESC' key is pressed or the video ends.

**Releases the video capture object**: The code releases the video capture object and closes all open windows using the cap.release() and cv.destroyAllWindows() functions, respectively.

**Output of object tracking opencv**




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## Conclusion

* In conclusion, object tracking opencv is a crucial task in computer vision and has numerous applications in various fields such as surveillance, autonomous driving, and robotics.
* OpenCV provides a range of powerful tools for object tracking that can be implemented with ease.
* The implementation discussed here covers the basics of object tracking and provides a good foundation to build upon.
* With further research and experimentation, it is possible to improve the accuracy and efficiency of object tracking in real-world scenarios.

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## MCQs

**1. What is the primary difference between object detection and object tracking?**
a) Object detection predicts the presence of objects in an image, while object tracking follows objects through a video or a sequence of images.
b) Object detection identifies specific objects in an image, while object tracking predicts the trajectory of objects in a video.
c) Object detection relies on deep learning, while object tracking relies on computer vision techniques.
d) Object detection can be performed in real-time, while object tracking requires offline processing.
**Answer: a) Object detection predicts the presence of objects in an image, while object tracking follows objects through a video or a sequence of images.**

**2. What is the main challenge in object tracking in crowded scenes?**
a) Occlusion
b) Scale variation
c) Illumination changes
d) Motion blur
**Answer: a) Occlusion**

**3. Which of the following is a deep learning-based approach to object tracking?**
a) Optical flow
b) CAMShift
c) MOSSE
d) Siamese networks
**Answer: d) Siamese networks**

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