Object Detection and its Relation to Video Redaction
With the rise of AI technology, video editing software programs are more prevalent in the marketplace than ever before. As many of these programs feature automatic functionality, these software offerings must also be able to identify specific objects within video recordings. This identification of objects within videos is known as object detection. Object detection is defined as the ability of computer software programs to identify certain objects within video recordings, pictures, or PDF files, based upon certain categories or classes. These classes can include objects such as faces, license plates, people, screens, or pieces of paper, among many others. This is predicated on the idea that every object class has its own distinct set of features or characteristics.
To give a practical example of object detection, consider a set of images containing a human being, a dog, and a cat. Through object detection, computer software programs are not only able to discern that these are three distinct objects that vary from one another but also determine and track the precise location of these objects within a set of images. This is done in accordance with the specific sets of features that can be found in specific object classes.
A primary issue that often arises during the object detection process is the problem of classifying and finding a variable number of objects within a video or image. For example, it may be easy for an object detection software program to identify a man’s face and a license plate in a video recording that contains a single person and car. Alternatively, identifying all of the faces and license plates in a video containing 40 people and 40 cars would prove more difficult as it’s more likely that the objects are smaller and further from view. What’s more, the number of objects in a given data set or recording may change in an instant. A video may begin with a single person driving a car down a residential street, and then transition to that same man driving along dozens of other drivers on a busy interstate highway.
Different types of object detection
Broadly speaking, object detection can be broken down into two categories, machine learning based approaches and deep learning based approaches. In traditional machine learning based approaches, computer vision techniques are used to look at the various features of an image, such as edges or color histograms. The features of these images are then used to identify groups of pixels that may belong to a particular object. These features are then fed into a regression model that predicts the location of the object within the image while also providing a label for the object.
Alternatively, deep-learning based approaches make use of convolutional neural networks or CNNs to perform end-to-end unsupervised object detection, in which features do not need to be defined and extracted separately. A CNN is a multilayered neural network with a special architecture used to detect complete features within a data set. They have been used in self-driving vehicles, powering vision in robots, and image recognition software. Once a CNN model has been built, it can also be used to classify the contents of different images. Advanced video redaction software programs make use of CNNs to assist in detecting images.
Object detection in Video Redaction Software
As automatic video redaction software programs are now readily available to customers, redacting video content has been made easier than ever before. A large component of this is AI and machine learning capabilities that enable these programs to pick up objects within a video recording automatically, without any further input from the user. The software’s ability to pick up these various items across the course of a video hinges upon the concept of object detection, or a machine learning algorithm’s ability to locate the presence of objects with some form of bounding box and applicable classes regarding the items being displayed.
From these bounding boxes and different classifications of objects, automatic video redaction software programs are able to recognize what objects need to be redacted from video footage. When using such software options, users are able to select items to redact from a dropdown menu. What’s more, users are also able to apply effects over these redactions, with the goal of removing as much personal information as possible. However, this entire process hinges on objects being accurately detected in the first place. As such, these automatic video redaction software programs will provide a confidence level concerning each detected object that is picked up. In this way, users can ensure that they are not detecting objects outside of their inputs.
With this confidence level, users of automatic video redaction software can rest assured that they are indeed picking up certain objects in a video. This confidence level will be between 0% to 100%, and a thumbnail will also be provided of the exact place in the video in which the detection took place. Users can then go to this spot in the video and observe what exactly was detected in the first place. If users find that an item has been mislabeled through the process, they have the choice to disable it from the project. With the help of object detection, the automatic video redaction process is made as intuitive and comprehensive as possible.
Watch the two minutes video below to see how automatic object detection in a video redaction software works.
While video redaction was once a labor-intensive and time-consuming process, advancements in the object detection process have made the video redaction process easier than ever before due to automation. Now instead of having to manually identify and redact objects throughout a hectic video recording, software programs can now do this same process automatically. In this way, consumers can save valuable time and resources when conducting their work operations. Moreover, they can rest assured that the job is being completed in the most efficient and effective manner possible at all times.