Recall the last time you were watching a live football match in a playground. As the ball was being kicked around, your eyes followed its movement and the players’ movement very swiftly. Your eyes did something called object tracking.
You knew the ball was being kicked from Player A to Player B and if Player C from the rival team intervened and took control of the ball, you were able to identify that too. After some time, you could even predict where the ball was likely to go based on its movement.
However, you not only watch live matches in a stadium or a playground but also take to television screens to witness a good soccer battle. Even then, your eyes and the camera follow the movement of the ball so swiftly, it almost feels like you are watching a live match right before your eyes. Have you ever wondered how this happens?
Hint: it’s not always manually controlled. That’s right, this is because of deep learning and specifically one of its applications called object tracking.
What is object tracking?
Object tracking is an application of deep learning where images of certain objects are first detected by the program. The program then goes on to develop a unique identification for each of the detections. When these objects move, this movement is then tracked by the program.
In simple terms, object tracking is the process of identifying moving objects in a video.
Thanks to state-of-the-art technology that is accessible today, machines can not only detect identifiable objects in motion, but also predict the future position of said 'data point' in upcoming frames.
How does it work?
Jargon aside, the underlying concept behind the process of object tracking is rather easy to understand.
- First, the algorithm needs to identify the target before beginning to track its movement. The initial state of the object is determined and usually, a tracking box is drawn around the object.
- Then, the features of the object are mapped as it moves because the appearance needs to be learnt so that the understanding of the object remains intact even with movement.
- The next step is to estimate and predict the likely area where the object is going to be in every subsequent frame. After this, the region marked as most likely for the object to be as it moves is scanned to pinpoint its exact location.
Types of object tracking
- Video tracking
- Visual tracking
- Image tracking
What kinds of object tracking algorithms exist?
There are two levels of object tracking- single object tracking and multiple object tracking.
Single object tracking algorithms follow the movement of a single object across the screen. This occurs even if there are multiple objects in the frame. The target object to be tracked is decided upon in the first frame of the video.
Multiple object tracking algorithms follow the movement of multiple objects across the screen. It can even identify and track new objects that enter and exit the frame.
Besides this, tracking algorithms are also classified based on whether they are offline or online.
Online trackers are pretty much real-time, which means that the predictions to create movement trajectories are immediately available. The one disadvantage of this is that future frames cannot be used to refine the results.
You use offline tracking algorithms after you have recorded a video stream. When you have to track an object or multiple objects in a recorded stream, offline trackers work to track the movement. These are helpful because future frames are at your disposal to make accurate predictions about the movement of the object.
We can also categorize algorithms based on whether they include detectors or not.
Detection based trackers have a detection hypothesis to form trajectories to track moving objects. These detectors are pre-trained to identify certain objects. This means that these trackers can also quickly detect any new objects moving in and out of the frame.
Detection free tracking, on the other hand, requires the programmer to manually initialize the objects in the first frame. The tracker then identifies and tracks these objects in the remaining frames.
This is not very useful especially if the scene the tracker is capturing involves a lot of target objects moving in and out of the screen because this method of tracking cannot deal with that.
Applications of object tracking
Widely used in computer vision, surveillance and security frequently make use of object tracking technology. It is easy to track the movement of people in and out of an area using a surveillance camera.
This is what is done in public spaces like shopping malls and department stores.
Because of the shapes (squares and rectangles) that are often created around the objects that are being tracked, object pathways are also created.
The same is useful for sports match analysis to determine the movement of one or more players to identify any foul points.
Traffic management, visual tracking and prediction of general movement, medical imaging and augmented reality all make use of object tracking.
Some of the limitations or challenges of object tracking algorithms are:
- If the video is captured by a low-resolution camera, the footage obtained will be of relatively poor quality. This makes it difficult for the tracking algorithm to track moving objects accurately.
- If there are similar objects in the frames moving all at once, then it is a challenge for the trackers to accurately create trajectories for each object. This includes objects that are of similar color, shape and size.
- Sometimes, even complex tracking algorithms cannot predict human movement because it can be so sudden and confusing. Many times, the tracking trajectories can overlap or cut off right in between because of sudden interference or unpredictability of movements.
- Very crowded backgrounds can affect how the object tracking algorithm tracks even a single object’s movement. This is especially so if we need to track a smaller object. It’s also possible that the algorithm picks up a new object as the one it has been tracking for a while just because of its proximity.
These algorithms, when used effectively to their fullest potential, can reduce considerably, but not entirely replace human labor.
With algorithms that can do smart and complex things like this, we are making major strides in AI and deep learning, yet there is a lot more waiting to be invented and discovered.
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