Object Detection Using Live Video Feeds

Business Challenge

Many businesses have security systems in place to protect their workers and valuables, with a common practice being security cameras inside and outside a facility. These cameras stream live video feed during the entire day, and sometimes at night depending on the business’s needs. Security cameras normally require a person to monitor the live feed, however computers can be taught to perform that task as well.

Object recognition is computationally demanding problem for a machine. The majority of the issue lies in the variation of an image, since any object can create an infinite number of different images based on its position, orientation, size and lighting. Identifying a car in a parking lot is a simple task for a human being that becomes significantly more difficult for a machine. The real-time aspect of a video, essentially many sequential images, means that any object detection algorithm used must execute almost instantaneously.


SFL's Approach

SFL’s approach was to develop software that swiftly and accurately classified the objects in a video. We needed to detect several moving objects at once, so we isolated the objects of interest by removing the extraneous background information. A convolutional neural network was chosen as our model due to its superior capabilities for modelling image recognition.  Potential scenarios included car crashes and people fighting, and was generalized to cover a variety of situations. After training and parameter tuning, this model could systematically detect and tag important objects. The speed of the neural network model allowed for the recognition of objects on a real-time video feed.


Business Value

This automated system for tagging objects greatly reduces the need for constant video monitoring. Without the need for a human camera monitor, thousands of work hours can be saved. When the software detects an emergency scenario, and is certain of it, the authorities are automatically alerted to the situation. Otherwise it saves its findings to be reviewed by a human operator at a later point in time. As an added security measure, the facial recognition aspect of this software can populate a database of recurring visitors and use that information in its object tagging. SFL provides a tool that not only accurately detects and reports important scenarios, but improves itself over time to better suit its environment.