There are two primary kinds of object tracking, first, image tracking is the process of isolating objects in an image and tracking them.
Advances in AI and machine learning are presenting opportunities to analyze videos with automated tools. A video captured by a self-driving car may include other vehicles, pedestrians, buildings, and cyclists but some objects, like buildings, do not move while pedestrians and cyclists do. It is important to be able to track how they move so the self-driving car can avoid them. This requires a technique known as object tracking.
There are two primary kinds of object tracking that serve different kinds of applications. First, image tracking is the process of isolating objects in an image and tracking them. For instance, real estate agents might use this in interior design software. The second is video tracking. This fully utilizes machine learning object tracking by tracing the movement of dynamic objects through a scene, such as vehicles and pedestrians. But although this technology can boost the efficiency and power of many applications, it comes with some challenges.
It is important to distinguish the different terms in this area. Object detection, which is not the same as object tracking, identifies an object in frame but does not take into consideration its past position or possible future positions, such as where a pedestrian walking across a street will be a few seconds in the future. Object detection is not concerned with knowing that a person at one position in a video frame is also the same person in a different position in a frame from a later time; instead, it is tasked with labeling the object as a car, person, bicycle, etc.This means object tracking is better suited for tracking a specific object's position and trajectory over time, while object detection can count similar objects identified by a common identifying label, such as person or vehicle.
Analyzing images and videos using ML has a wide variety of applications. Here are just some of the ways organizations can utilize ML-powered computer vision:
While there are multiple applications for object tracking, there are some challenges that are common to all of them.
There are many hurdles that can get in the way of functional object tracking applications, and most of them have to do with analyzing highly complex scenes. In some cases, there are many objects in a frame, which can make it difficult to accurately track all of the objects. In addition, objects are sometimes blocked from the view of the camera because another object moved.Object tracking algorithms can sometimes lose track of objects after the tracked object becomes partially hidden or temporarily leaves the frame. This problem is less prevalent in software with fast detection and labeling capabilities.Similarly, object tracking algorithms can lose track of objects or mislabel objects and then track them incorrectly. This is most common in scenes with many similar objects or small objects that can confuse the algorithm.Training complex algorithms, such as object tracking, takes a tremendous amount of time and work hours. This issue is helped by finding ways to reduce training time, often with the help of third-party organizations that specialize in computer vision applications.
Frame-level object tracking can be applied to video or 3D point clouds typically used with light detection and ranging (Lidar), a sensing technology used on objects in motion. When objects are tracked across frames, it is important to be able to label fixed characteristics, such as the entity type, eg a car or a human being, and variable characteristics, such as pose or spatial orientation.Object tracking can be focused on a single object or multiple objects. In the case of single object tracking, one object is identified and its movement or change is tracked from frame to frame. In the case of multiple-level tracking, the ML system first identifies multiple objects in a frame and tracks each individually.
While these are difficult problems to solve, there are solutions.Precision and efficiency are two of the most important aspects of a good object tracking solution. Sama's ML object tracking, for instance, uses human-in-the-loop input to provide initial accuracy and then empowers prediction and annotation features with machine learning. For more information, read our recent blog post on Sama's ML object tracking solution.
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