README.md

Vehicle Detection

The goals / steps of this project are the following:

• Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a Linear SVM classifier
  • Apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector.
• Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
• Run your pipeline on a video stream (start with the test_video.mp4 and later implement on full project_video.mp4) and create a heat map of recurring detections frame by frame to reject outliers and follow detected vehicles.
• Estimate a bounding box for vehicles detected.

Rubric Point

• Here I will consider the rubric points individually and describe how I addressed each point in my implementation.

*** File submission inculde all the required files that are necessary to quialy the project submission ***

1. project.ipynb
2. Readme.md
3. Writeup.

Below are the steps described individually that are implement in the project

1. HOG feature
2. Extract Feature(SVC , CLF Results)
3. Sliding Windows
4. Pipeline Explanation

1) HOG feature is called Histogram of Oriented Gradient

• It has very import task in prediction as it helps to get
• It mainly consist of gradient , magnitued and direction
• Grouping these individual values into small group of cells
• And classifing from almost 9 orients bins to get the result
• and then combining again the each pixel

#### 2) Extraction Feature * It combine spatial feature , hist_feature and hog_feature * below are the parameter used by me for bestresults --- ```python

color_space = 'YCrCb' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9  # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
spatial_size = (32, 32) # Spatial binning dimensions
hist_bins = 32    # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off

---

      * Later is applied SVC and CLF , I prefered CLF over SVC as it has more accuray in practical over CVC while i            was using it in my project video in order to detect the cars   
 
_*result after extracting feature and applying SVC along with CLF *_ 
---

Using: 9 orientations 8 pixels per cell and 2 cells per block Feature vector length: 8460 0.7 Seconds to train SVC... Test Accuracy of SVC = 1.0 179.53 Seconds to train CLF... Test Accuracy of SVC = 1.0 My CLF predicts: [ 0. 0. 1. 1. 1. 1. 0. 0. 0. 0.] For these 10 labels: [ 0. 0. 1. 1. 1. 1. 0. 0. 0. 0.] 0.078 Seconds to predict 10 labels with CLF

---
####  3) Sliding Windows

* For sliding window mutliple function has been used like sliding_windows function itself, search_windows which further consist of single_img_features but I have used another function such as find_cars() which was used  during the process of reading frame one by one and applying prediction 


<img src="./output_images/sliding_window.png" width='400'/>

<img src="./output_images/search_windows_img.png" width='400'/>



search_windows_img.png
#### 4) Pipeline Explanation

* process_pipe is the function in which on fund car function is applied vaious time in various axis in order to find the sorrounding cars . rectangles is the varable taken to compress all the rectanges into one rectange which are appended in rects variable of list , using that reactange list I applied add_heat to apply head signle like and then applied threshold on them using apply_threshold  and got the expected result and return the img


<img src="./output_images/final_img.png" width='800'/>

##### test video
<video width="400" controls>
  <source src="test_video_out.mp4.mp4" type="video/mp4">
</video>

##### project video
<video width="400" controls>
  <source src="project_video_out.mp4.mp4" type="video/mp4">
</video>



```python