Object tracking has become a cornerstone task in computer vision, finding its way into applications like robotics, video surveillance, autonomous vehicles, augmented reality, and human-computer interaction. This guide dives into the fundamentals of object tracking, explores its challenges, and presents the best AI models and tools to automate and enhance the process.

What is Object Tracking?

Object tracking is the process of predicting the position of a target object across multiple frames in a video. This task involves detecting objects in each frame and associating them to create continuous trajectories. While this may sound straightforward, the volume of data involved in even short videos makes object tracking a computationally intensive task. For example, a single hour-long video at 24 frames per second (FPS) contains 86,400 frames. Tracking 8-12 objects per frame can lead to over a million bounding boxes to annotate and manage.

Thanks to advances in AI and automated tools, object tracking has become faster, more reliable, and less reliant on manual labor. However, selecting the right tools and approaches remains critical to achieving efficiency and accuracy.

Key Subtasks in Visual Object Tracking

1. Single Object Tracking (SOT)
Single Object Tracking focuses on identifying and tracking one object throughout a video. The process begins with manual annotation of the object in the first frame, after which a neural network tracks the object in subsequent frames.

Class-agnostic models like MixFormer and TransT are popular for SOT. These models work across different object types, making them flexible for use in video annotation tools like Supervisely Video Labeling Toolbox, where they help automate and speed up manual labeling processes.

2. Multiple Object Tracking (MOT)
Multiple Object Tracking handles detecting and tracking several objects of predefined classes simultaneously. Unlike SOT, where the user specifies the object, MOT models are trained on datasets to detect and track specific object classes autonomously.

The Tracking-by-Detection approach is widely used for MOT. In this method, an object detector identifies objects in each frame, and a tracking algorithm associates the detections across frames to create trajectories. Popular tools like YOLOv8 combined with DeepSort or BoT-SORT algorithms excel in this process, enabling high accuracy and flexibility for various applications.

3. Semi-Supervised Class-Agnostic Multiple Object Tracking
Semi-supervised tracking combines the flexibility of SOT with MOT’s ability to track multiple objects. It allows users to label custom objects on the first frame, then apply AI assistance to track these objects across the remaining frames. Users can intervene to correct inaccuracies, ensuring higher annotation quality without sacrificing speed.

4. Video Object Segmentation (VOS)
Video Object Segmentation goes beyond bounding boxes by tracking the object’s mask across video frames. Starting with manual labeling of an object mask on the first frame, AI models like XMem and tools like Segment Anything segment and track the object automatically in subsequent frames. This technique is particularly useful in applications requiring precise object boundaries.

Challenges in Object Tracking

Object tracking is challenging due to its reliance on large datasets and computational resources. Annotating videos manually is time-consuming, especially when tracking multiple objects over thousands of frames. Additionally, the performance of tracking models depends heavily on the quality of training data. Models designed for specific classes may require retraining when new objects are introduced, adding to the complexity.

Furthermore, the choice between end-to-end models and the tracking-by-detection paradigm introduces trade-offs. While end-to-end models are faster, they require extensive labeled video data for training. In contrast, the tracking-by-detection approach is modular and easier to update, but it is slower due to reliance on multiple models.

Best Tools and AI Models for Object Tracking

• YOLOv8
  As a leading object detection model, YOLOv8 is widely used for object tracking. Integrated with platforms like Supervisely, YOLOv8 simplifies video annotation by allowing users to train and deploy detection models for custom datasets.

• DeepSort and BoT-SORT
  These algorithms excel in associating detections across video frames, enabling efficient multi-object tracking. By integrating them with detection models like YOLO, users can achieve robust tracking pipelines.

• Supervisely Video Labeling Toolbox
  A comprehensive platform for object tracking, Supervisely supports state-of-the-art models like MixFormer and XMem. It offers both manual and automated tools for video annotation, making it ideal for research and production workflows.

Approaches to Object Tracking

• End-to-End Models
  These models solve detection and tracking simultaneously using a single neural network. This approach is faster during inference but less flexible as it requires fully labeled videos for training.

• Tracking-by-Detection
  This method involves using separate models for detection and tracking. For instance, a detection model predicts bounding boxes, and a tracking algorithm associates these predictions across frames. This modular approach is easier to customize and update but slower than end-to-end models.

Practical Applications and Tutorials

Platforms like Supervisely offer hands-on tutorials and applications demonstrating how to implement object tracking workflows. Tutorials cover training models like YOLOv8, deploying them as REST APIs, and integrating tracking algorithms like DeepSort for automated video annotation.

Conclusion

Object tracking has seen significant advancements, enabling faster, more accurate results through AI-powered tools and frameworks. By understanding its subtasks and leveraging state-of-the-art technologies, users can optimize their workflows for diverse applications. Platforms like Supervisely provide an accessible entry point, offering free resources and tools to help users build and deploy their computer vision solutions.

Whether you’re working on a research project or implementing tracking in production, adopting the right tools and methodologies will ensure success. Start exploring Supervisely and its advanced features to see the impact of modern AI on object tracking workflows.