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diff --git a/README.md b/README.md
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-# Vehicle Detection Project
+# Vehicle Detection
 [![Udacity - Self-Driving Car NanoDegree](https://s3.amazonaws.com/udacity-sdc/github/shield-carnd.svg)](http://www.udacity.com/drive)
 
 
@@ -10,24 +10,24 @@ A great writeup should include the rubric points as well as your description of
 
 All that said, please be concise!  We're not looking for you to write a book here, just a brief description of how you passed each rubric point, and references to the relevant code :). 
 
-You're not required to use markdown for your writeup.  If you use another method please just submit a pdf of your writeup.
+You can submit your writeup in markdown or use another method and submit a pdf instead.
 
 The Project
 ---
 
 The goals / steps of this project are the following:
 
-* Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
-* Apply a distortion correction to raw images.
-* Use color transforms, gradients, etc., to create a thresholded binary image.
-* Apply a perspective transform to rectify binary image ("birds-eye view").
-* Detect lane pixels and fit to find the lane boundary.
-* Determine the curvature of the lane and vehicle position with respect to center.
-* Warp the detected lane boundaries back onto the original image.
-* Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
+* Perform a Histogram of Oriented Gradients (HOG) feature extraction on a labeled training set of images and train a classifier Linear SVM classifier
+* Optionally, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector. 
+* Note: for those first two steps don't forget to normalize your features and randomize a selection for training and testing.
+* Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
+* Run your pipeline on a video stream 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.
 
-The images for camera calibration are stored in the folder called `camera_cal`.  The images in `test_images` are for testing your pipeline on single frames.  To help the reviewer examine your work, please save examples of the output from each stage of your pipeline in the folder called `ouput_images`, and include a description in your writeup for the project of what each image shows.    The video called `project_video.mp4` is the video your pipeline should work well on.  
+Here are links to the labeled data for [vehicle](https://s3.amazonaws.com/udacity-sdc/Vehicle_Tracking/vehicles.zip) and [non-vehicle](https://s3.amazonaws.com/udacity-sdc/Vehicle_Tracking/non-vehicles.zip) examples to train your classifier.  These example images come from a combination of the [GTI vehicle image database](http://www.gti.ssr.upm.es/data/Vehicle_database.html), the [KITTI vision benchmark suite](http://www.cvlibs.net/datasets/kitti/), and examples extracted from the project video itself.   You are welcome and encouraged to take advantage of the recently released [Udacity labeled dataset](https://github.com/udacity/self-driving-car/tree/master/annotations) to augment your training data.  
 
-The `challenge_video.mp4` video is an extra (and optional) challenge for you if you want to test your pipeline under somewhat trickier conditions.  The `harder_challenge.mp4` video is another optional challenge and is brutal!
+Some example images for testing your pipeline on single frames are located in the `test_images` folder.  To help the reviewer examine your work, please save examples of the output from each stage of your pipeline in the folder called `ouput_images`, and include them in your writeup for the project by describing what each image shows.    The video called `project_video.mp4` is the video your pipeline should work well on.  
 
-If you're feeling ambitious (again, totally optional though), don't stop there!  We encourage you to go out and take video of your own, calibrate your camera and show us how you would implement this project from scratch!
+**As an optional challenge** Once you have a working pipeline for vehicle detection, add in your lane-finding algorithm from the last project to do simultaneous lane-finding and vehicle detection!
+
+**If you're feeling ambitious** (also totally optional though), don't stop there!  We encourage you to go out and take video of your own, and show us how you would implement this project on a new video!
diff --git a/examples/HOG_example.jpg b/examples/HOG_example.jpg
diff --git a/examples/car_not_car.png b/examples/car_not_car.png
diff --git a/examples/img50.jpg b/examples/img50.jpg
diff --git a/examples/sliding_window.jpg b/examples/sliding_window.jpg
diff --git a/examples/sliding_windows.jpg b/examples/sliding_windows.jpg
diff --git a/output_images/.DS_Store b/output_images/.DS_Store
diff --git a/output_images/save_output_here.txt b/output_images/save_output_here.txt
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+Please save your output images to this folder and include a description in your README of what each image shows.
diff --git a/project_video.mp4 b/project_video.mp4
diff --git a/test_images/.DS_Store b/test_images/.DS_Store
diff --git a/test_images/test1.jpg b/test_images/test1.jpg
diff --git a/test_images/test2.jpg b/test_images/test2.jpg
diff --git a/test_images/test3.jpg b/test_images/test3.jpg
diff --git a/test_images/test4.jpg b/test_images/test4.jpg
diff --git a/test_images/test5.jpg b/test_images/test5.jpg
diff --git a/test_images/test6.jpg b/test_images/test6.jpg
diff --git a/writeup_template.md b/writeup_template.md
@@ -0,0 +1,97 @@
+##Writeup Template
+###You can use this file as a template for your writeup if you want to submit it as a markdown file, but feel free to use some other method and submit a pdf if you prefer.
+
+---
+
+**Vehicle Detection Project**
+
+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 classifier Linear SVM classifier
+* Optionally, you can also apply a color transform and append binned color features, as well as histograms of color, to your HOG feature vector. 
+* Note: for those first two steps don't forget to normalize your features and randomize a selection for training and testing.
+* Implement a sliding-window technique and use your trained classifier to search for vehicles in images.
+* Run your pipeline on a video stream 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.
+
+[//]: # (Image References)
+[image1]: ./examples/car_not_car.png
+[image2]: ./examples/HOG_example.jpg
+[image3]: ./examples/sliding_windows.jpg
+[image4]: ./examples/sliding_window.jpg
+[image5]: ./examples/img50.jpg
+[image6]: ./examples/example_output.jpg
+[video1]: ./project_video.mp4
+
+## [Rubric](https://review.udacity.com/#!/rubrics/513/view) Points
+###Here I will consider the rubric points individually and describe how I addressed each point in my implementation.  
+
+---
+###Writeup / README
+
+####1. Provide a Writeup / README that includes all the rubric points and how you addressed each one.  You can submit your writeup as markdown or pdf.  [Here](https://github.com/udacity/CarND-Vehicle-Detection/blob/master/writeup_template.md) is a template writeup for this project you can use as a guide and a starting point.  
+
+You're reading it!
+
+###Histogram of Oriented Gradients (HOG)
+
+####1. Explain how (and identify where in your code) you extracted HOG features from the training images.
+
+The code for this step is contained in the first code cell of the IPython notebook (or in lines # through # of the file called `some_file.py`).  
+
+I started by reading in all the `vehicle` and `non-vehicle` images.  Here is an example of one of each of the `vehicle` and `non-vehicle` classes:
+
+![alt text][image1]
+
+I then explored different color spaces and different `skimage.hog()` parameters (`orientations`, `pixels_per_cell`, and `cells_per_block`).  I grabbed random images from each of the two classes and displayed them to get a feel for what the `skimage.hog()` output looks like.
+
+Here is an example using the `YCrCb` color space and HOG parameters of `orientations=8`, `pixels_per_cell=(8, 8)` and `cells_per_block=(2, 2)`:
+
+
+![alt text][image2]
+
+####2. Explain how you settled on your final choice of HOG parameters.
+
+I tried various combinations of parameters and...
+
+####3. Describe how (and identify where in your code) you trained a classifier using your selected HOG features (and color features if you used them).
+
+I trained a linear SVM using...
+
+###Sliding Window Search
+
+####1. Describe how (and identify where in your code) you implemented a sliding window search.  How did you decide what scales to search and how much to overlap windows?
+
+I decided to search random window positions at random scales all over the image and came up with this (ok just kidding I didn't actually ;):
+
+![alt text][image3]
+
+####1. Show some examples of test images to demonstrate how your pipeline is working.  What did you do to try to minimize false positives and reliably detect cars?
+
+Ultimately I searched on two scales using YCrCb 3-channel HOG features plus spatially binned color and histograms of color in the feature vector, which provided a nice result.  Here are some example images:
+
+![alt text][image4]
+---
+
+### Video Implementation
+
+####1. Provide a link to your final video output.  Your pipeline should perform reasonably well on the entire project video (somewhat wobbly or unstable bounding boxes are ok as long as you are identifying the vehicles most of the time with minimal false positives.)
+Here's a [link to my video result](./project_video.mp4)
+
+
+####2. Describe how (and identify where in your code) you implemented some kind of filter for false positives and some method for combining overlapping bounding boxes.
+
+I recorded the positions of positive detections in each frame of the video.  From the positive detections I created a heatmap and then thresholded that map to identify vehicle positions.  I then used blob detection in Sci-kit Image (Determinant of a Hessian [`skimage.feature.blob_doh()`](http://scikit-image.org/docs/dev/auto_examples/plot_blob.html) worked best for me) to identify individual blobs in the heatmap and then determined the extent of each blob using [`skimage.morphology.watershed()`](http://scikit-image.org/docs/dev/auto_examples/plot_watershed.html). I then assumed each blob corresponded to a vehicle.  I constructed bounding boxes to cover the area of each blob detected.  
+
+Here's an example result showing the heatmap and bounding boxes overlaid on a frame of video:
+
+![alt text][image5]
+
+---
+
+###Discussion
+
+####1. Briefly discuss any problems / issues you faced in your implementation of this project.  Where will your pipeline likely fail?  What could you do to make it more robust?
+
+Here I'll talk about the approach I took, what techniques I used, what worked and why, where the pipeline might fail and how I might improve it if I were going to pursue this project further.  
+
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		Please save your output images to this folder and include a description in your README of what each image shows.