Online Object Tracking A Benchmark : The Studies
The primary subject of these studies is Online Object Tracking A Benchmark.
Visual object tracking in industrial settings: A study
A study about the performance of object tracking using various sensors is conducted in this special issue. The study finds that the visual object tracking can be highly beneficial for video gaming and other praised applications, but can also cause problems when applied to other industries such as rubble removal or manufacturing.
Robust Radar-Assisted Object Tracking using Adversarial Learning
A review about how artificial intelligence helps in object tracking has been conducted with adversarial learning. This study found that, using the adversarys knowledge, the artificial intelligence can quickly identify and track a specific object in a multimedia data set.
Sensor-Based Detection of Object Misplacement
A review about Sensors for Object Detection, Classification and Monitoring has been conducted in order to develop accurate detection algorithms. The study found that better sensors can help us get a better understanding of where objects are and how they change over time in images. By using these sensors, we can make it easier for us to determine the classification and location of objects.
Object Tracking and Analytics for Sustainable Business
An article about the use of object tracking system (OOTS) in business was conducted. The findings showed that object tracking can help organizations to obtain a better understanding of their environment and make better decisions. Many challenges still remain, as there are constantly changes in the appearance patterns of objects and unexpected motion within the scene. To overcoming these challenges, object tracking can be a great tool for businesses.
discriminative filter-based tracking
A study about visual object tracking using discriminative filters and siamese networks has been made. Results show that this approach is able to improve Tracking performance while also having better accuracy.
