chang-chia-chi/pedestrian-detection

Pedestrian Detection using Fast-R-CNN with Pytorch.

Pedestrian-Detection

This is my final project result of digital-image-processing course held by NCTU in 2020.

The goal is to detect pedestrian in pictures or video, and count approximate number of people in the scene. Using pre-trained Fast-R-CNN model and transfer learning, the performance is pretty good with only 260 training pictures[performance]. Because the problem scale is not big, Google Colaboratory is used for easy enviroment setting and free GPU.

Dataset

Two dataset are used, one is from pytorch official turtorial, the other is from widerperson and randomly select 100 pictures.

Dataset	Feature
Pytorch tutorial	Only two~three people in the picture.
WiderPerson	Tens of people in the picture, which will highly enhance ability of ai to identify people in crowd.

See link for training pictures and corresponding coco dataset json file.

CoCo Dataset JSON Format (reference)

CoCo is abbreviation of Common Objects in COntext, quote from cocodataset.org:

COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints.

Coco has been used for so many projects because it's one of best image dataset in the world. Below will shortly introduce basic structure of coco dataset format for your own training data. You could find example python code in link which illustrate how to convert annotation or mask information to json file.

Structure and section

Structure

{
    "info": info,
    "licenses": [license],
    "images": [image],
    "annotations": [annotation],
    "categories": [category]
}

section

1. Info: contains high level information about the dataset.

"info":{   
    "year": int,      
    "version": str,     
    "description": str,   
    "contributor": str,   
    "url": str,   
    "date_created": datetime,   
}   

2. licence: contains a list of image licenses that apply to images in the dataset.

"license":[
        {
        "id": int, 
        "name": str,  
        "url": str,    
        }   
]   

3. images: contains the complete list of images in your dataset.

image{    
    "id": int,    
    "width": int,   
    "height": int,    
    "file_name": str,   
    "license": int,   
    "flickr_url": str,    
    "coco_url": str,    
    "date_captured": datetime,    
}

4. annotation: contains a list of every individual object annotation from every image in the dataset.

annotation{   
    "id": int,    
    "image_id": int,
    "category_id": int,
    "segmentation": [], (Fast-R-Cnn does not need this information)
    "area": float,
    "bbox": [x,y,width,height],
    "iscrowd": 0 or 1,
}

This one is the most trickiest and important to understand, so a table collect purpose for each variable as below:

variable	purpose
id	annotation id
image_id	corresponding image's id
category_id	which category of the object belongs to
segmentation	segmentation information for Mask-R-CNN
area	area for marked object in the picture, usually computed by height * width of box
bbox	x, y are position of left corner of box; width and height are box dimension
iscrowd	specifies whether the segmentation is for a single object or for a group/cluster of objects

P.S. Every object marked has one annotation. So it's one to one relationship between object and annotation id.

5. catogories: contains a list of categories (e.g. dog, person) and each of those belongs to a supercategory (e.g. animal, human).

{   
    "id": int,    
    "name": str,    
    "supercategory": str,   
}