Vehicle Detection

Overview

In this project, we will use write a software pipeline to detect vehicles in a video (start with the test_video.mp4 and later implement on full project_video.mp4) however the main product is to create a detailed writeup of the project.

Goals

• Histogram of Oriented Gradients (HOG)
  • Feature extraction on a labeled training set of images
  • Train a classifier Linear SVM classifier using selected HOG features
• Sliding Window Search (SWS)
  • Implement a SWS technique
  • Use trained classifier to search for vehicles in images.
• Video Implementation
  • The SWS plus classifier has been used to search for and identify vehicles in the videos provided.
  • Implement some kind of filter for false positives and some method for combining overlapping bounding boxes.
• Reflection

Result

Project Video

Files

• Notbook
• Result

Loading and Visualizing the data

• Datasets provided by Udacity
  • Car Images
  • NonCar Images
  • Example of random sample images from the vehicle and non-vehicle datasets.

Histogram of Oriented Gradients (HOG)

Feature extraction from training images(HOW) and final parameters.

HOG FINAL PARAMETERS

color_space = 'YCrCb' 
orient = 9  
pix_per_cell = 8 
cell_per_block = 2
hog_channel = "ALL"
spatial_size = (16, 16) # Spatial binning dimensions
hist_bins = 16          # 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

The get_hog_features function takes in an image and computes the Histogram of Oriented Gradient (HOG). Takes image as input and HOG parameters (orientations, pixels_per_cell, cells_per_block)

Sample Result of Different HOG parameters

SVM Accuracy	Orientation	Pixels Per Cell	Feature Vector Length	Time
98.59%	11	16	2	4356
98.45%	9	16	2	4140
97.38%	11	16	2	4356
97.61%	9	16	2	4140
98.62%	9	8	2	8460

Color Space mainly used were RGB, YUV, YCrCb.

Accuracy and Time taken were considered when finalizing HOG Parameters

Histogram of Oriented Gradients (HOG)

Training classifier using selected HOG featres

SVM Classifier with the default classifier parameters using HOG features as indicated above was able to achieve test accuracy of 98.62%. Features were scaled to zero mean and unit variance before training the classifier using sklearn's StandardScaler (slection for training/testing radomised using np's random).

Sliding Window Search

Implementing sliding window search scales overlap windows.

Final Scales and window sizing

scales = [1, 1.5]
y_start_stops = [[380, 460], [380, 560]]

Different scales and windows found to be relevant as tried out in notebook

scales = [1, 1.5, 2, 2.5, 4]
y_start_stops = [[380, 460], [380, 560], [380, 620], [380, 680], [350, 700]]

After playing with sliding window the result was to use find_cars mthong from the lesson with bit of tweaking. cells_per_step = 1 used instead of overlap to define how many cells to step.

Sliding Window Search

Example of test images using pipeline and performance optimization

Performance was optimized by using

• heatmap
• threshold
• window sizing

Video Implementation

Link to final video where pipeline perform reasonably well.

• Project Video Input

• Project Video Output

• Pipeline perform reasonably well on the entire project video.

• Identifies vehicles most of the time.

• Minimum or no flase positives infact it even identifies vehicles coming from other side at times.

• Bit woobly and unstable bounding boxes at times.

Video Implementation

Some kind of filter for false positives and some method for combining overlapping bounding boxes

• find_cars does add_heat for heatmap
• find_cars does apply_threshold for threashold
• find_cars which does cells_per_step to combining instead of overlapping for bounding boxes.
• process_image does scales and window sizing

Reflection

Problems and issues faced and how could it be made more robust.

• Balancing the accuracy of the classifier with execution speed was the key.
  • If you do more on accuracy you start to drop boxes
  • If you do more on speed you end having false positives.
• Probably do better in presence of shadows
• Boxes could be better transition form one frame to another.