Open Source API for Computer Vision

Using the yolo/tiny-yolo models built on darkflow (the tensorflow translation of darknet), we created operation-goose, a computer vision API which logs objects, motion, and anomalies in natural language and outputs numerical logs (csv form) and visualization of activity over time..

What It Does

Users can upload files via the operation-goose API and receive a corresponding text file and time-series analysis summarizing file meta-data:

     1. Object counts at distinct time points
     2. Time stamps of significant change to camera feed
     3. Object quantities, appearances, and exits over time

The received video summary offers a significant use case in the processing of long-term surveillance footage, highlighting solely those times in which activity is detected in the frame.

All detected instances are logged with a time stamp and annotated with natural language:

     e.g. 34.51 s:
     1 car enters the frame.
     1 truck exits the frame.
     2 people enter the frame.

     Total objects detected:
     cars: 8
     motorbikes: 2
     people: 2
     trucks: 2

How We Built It

As mentioned above, object detection was done via yolo/tiny-yolo models, with counts and labels fed into various Python scripts designed to detect major changes and bouts of activity in frames. The server backend was built with Flask. Built for the CANSOFCOM challenge.

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