The dataset contains "Frames" directory containing ".jpg" images sequences, Ground Truth "mall_gt.mat" and other relevant files. More information regarding the dataset is provided in the "readme.txt". The links for the dataset and relevant research is given in the "About" section of this hackathon page.

The output of the model should be head positions for person co-ordinates in an image and their associated class ("Safe Distance", "Distance Violation").

The final submission should include:

Model Files (in formats supported by the frameworks given below)
Training Files (.ipynb, .py)
requirements.txt (providing details of modules required to run your submission)
Code Execution file (run.py)

Other files may be included for purposes of code modularity.

The file should be named “run.py” and will be supplied a “--testdir” named command line argument for evaluation, which will contain the images in "jpg" format, the example is given below:

run.py —testdir <path to test directory>

The output file should be named "output.json" having the nested format given below:

{"<file name 1>":

{"location of head position1 (x, y)" : "<class>",

"location of head position2 (x, y)" : "<class>",

"location of head position3 (x, y)" : "<class>",

},

"<file name 2>":

{"location of head position1 (x, y)" : "<class>",

"location of head position2 (x, y)" : "<class>",

},

...

}

output.json

{

“1.jpg”: {

“(10, 200)“: “Safe Distance”,

“(200, 300)“: “Safe Distance”

},

“2.jpg”: {

“(10, 200)“: “Safe Distance”,

“(200, 300)“: “Safe Distance” }}

Supported Frameworks:

Currently only Python-framework based models are accepted:

Tensorflow
Pytorch
Keras
Caffe2
Onnx

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