Vehicle Detection Project

# Vehicle Detection Project

### Abstract

The goal of the project was to develop a pipeline to reliably detect cars given a video from a roof-mounted camera: in this readme the reader will find a short summary of how I tackled the problem.

**Long story short**:

 - (baseline) HOG features + linear SVM to detect cars, temporal smoothing to discard false positive

 - (submission) [SSD deep network](https://arxiv.org/pdf/1512.02325.pdf) for detection, thresholds on detection confidence and label to discard false positive 

 

*That said, let's go into details!*

### Good old CV: Histogram of Oriented Gradients (HOG)

#### 1. Feature Extraction.

In the field of computer vision, a *features* is a compact representation that encodes information that is relevant for a given task. In our case, features must be informative enough to distinguish between *car* and *non-car* image patches as accurately as possible.

Here is an example of how the `vehicle` and `non-vehicle` classes look like in this dataset:

The most of the code that relates to feature extraction is contained in [`functions_feat_extraction.py`](functions_feat_extraction.py). Nonetheless, all parameters used in the phase of feature extraction are stored as dictionary in [`config.py`](config.py), in order to be able to access them from anywhere in the project.

Actual feature extraction is performed by the function `image_to_features`, which takes as input an image and the dictionary of parameters, and returns the features computed for that image. In order to perform batch feature extraction on the whole dataset (for training), `extract_features_from_file_list` takes as input a list of images and return a list of feature vectors, one for each input image.

For the task of car detection I used *color histograms* and *spatial features* to encode the object visual appearence and HOG features to encode the object's *shape*. While color the first two features are easy to understand and implement, HOG features can be a little bit trickier to master.

#### 2. Choosing HOG parameters.

HOG stands for *Histogram of Oriented Gradients* and refer to a powerful descriptor that has met with a wide success in the computer vision community, since its [introduction](http://vc.cs.nthu.edu.tw/home/paper/codfiles/hkchiu/201205170946/Histograms%20of%20Oriented%20Gradients%20for%20Human%20Detection.pdf) in 2005 with the main purpose of people detection. 

The bad news is, HOG come along with a *lot* of parameters to tune in order to work properly. The main parameters are the size of the cell in which the gradients are accumulated, as well as the number of orientations used to discretize the histogram of gradients. Furthermore, one must specify the number of cells that compose a block, on which later a feature normalization will be performed. Finally, being the HOG computed on a single-channel image, arises the need of deciding which channel to use, eventually computing the feature on all channels then concatenating the result.

In order to select the right parameters, both the classifier accuracy and computational efficiency are to consider. After various attemps, I came up to the following parameters that are stored in [`config.py`](config.py):

```

# parameters used in the phase of feature extraction

feat_extraction_params = {'resize_h': 64,             # resize image height before feat extraction

Author : ss infotech

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