When it comes to a computer vision task, it involves identifying as well as locating objects in videos and images. Therefore, it becomes a relevant part of different applications including robotics, self-driving cars, & others. Object tracking can be defined as the process of tracking detected objects throughout frames harnessing their temporal & spatial features. In this blogpost, we are going to delineate clear information about the way DeepSort works. In this context, we need to comprehend the elements of object tracking and the relevant innovations for DeepSORT. The popular object tracking algorithms involve SORT and DeepSORT with YOLO-NAS. Additionally, we will explore how YOLO-NAS for object tracking can be used for custom datasets.
Surveillance cameras play a vital role in securing our business atmosphere and home. For real time monitoring, computer vision and AI can be used to detect objects through frames.
Deep learning tracking is delineated as the task of predicting the varied positions of objects throughout the entire video utilizing their temporal & spatial features. Tracking is acquiring the initial set of detections & tracking them throughout video frames at the time of maintaining the assigned ids.
Object tracking has several benefits and real-world applications.
Tracking can be used in different sports for tracking the movement of balls & players.
Tracking is used for monitoring traffic and tracking vehicles movement on the road. It is highly beneficial for evaluating traffic & detecting vehicles.
Two major categories of trackers involve single and multiple object trackers.
SORT is considered as an approach that tracks objects and comprises four key elements comprising estimation, detection, creation & deletion and data association of track identities. SORT has great performance when it comes to tracking precision and accuracy however, it includes certain restrictions.
SORT algorithm returns tracks with a high number of identity switches. Among the limitations involve its inability to confront association matrices as whenever it is used it fails.
DeepSORT can be defined as a multiple object tracking algorithm that enhances the accuracy and efficiency of a DeepSORT system. It can recognize and track objects by applying an advanced association metric that integrates both motion and appearance descriptors. This integration reduces the identity switches promoting a more efficient tracking mechanism. It can stand out as a brief tracking algorithm in computer vision.