What is Object Tracking?
Object tracking is a subfield of computer vision that focuses on continuously identifying and following objects as they move across frames in a video. Unlike object detection, which identifies and locates objects in individual frames, object tracking maintains the identity of the detected objects across multiple frames, allowing for the analysis of their movement and behavior over time.
Difference from Object Detection:
While object detection and object tracking are closely related, they serve distinct purposes within computer vision. Object detection involves identifying and locating objects within individual frames of an image or video. It provides a snapshot of all objects present at a particular moment, outputting bounding boxes and class labels for each detected object. However, it does not maintain the identity of objects across multiple frames. Object tracking goes beyond detection by maintaining the identity of objects as they move across frames. After detecting objects in an initial frame, tracking algorithms follow these objects throughout the video, ensuring continuity and enabling the analysis of their trajectories and behaviors. Another key difference, although quite self-explanatory, is that object tracking cannot be used on single images as it relies on tracking objects across multiple frames of a video.
How it Works:
The mechanisms behind an object tracking system are extremely similar to those behind object detection.
Annotation:
Accurate annotation of training data is essential. This involves labeling objects in the initial frames with bounding boxes and possibly identifying specific features or key points.
Training the Model:
Initially, an object detection model is trained to recognize and locate objects within frames. Following detection, a tracking model is trained to follow the detected objects across frames. This involves training a tracker on features such as appearance, motion, or a combination of both.
Workflow:
An object detector identifies and localizes objects in the first frame or periodically throughout the video. Features of the detected objects are then extracted from the frame, such as color, shape, and texture. Based on the extracted features, the tracker predicts the object's location in subsequent frames. The tracker then updates its model with the new information from the predicted location, refining the object's position and trajectory.
Specific Steps Within The Model:
The actual steps involved within the tracking process are essentially the same as the object detection process. They are as follows:
Preprocessing: The input video frames are converted to the format required by the neural network, resized, and color-adjusted.
Input: The detection process starts with the preprocessed input frame, which is fed into the neural network.
Feature Extraction: The input frame passes through multiple convolutional layers that apply filters to detect various features like edges, textures, and patterns. Pooling layers reduce the spatial dimensions of the feature maps while retaining the most important information.
Classification and Localization: Each proposed region is further processed to refine the bounding box coordinates and classify the object within the box.
Post-Processing: Non-Maximum Suppression is applied to remove redundant bounding boxes, retaining only the most confident detections.
Industry Use Cases:
Ingress and Egress (https://www.lotuslabs.ai/portfolio-projects/pmy)
- Tracking and counting the number of people or objects entering or exiting a certain location
- Useful in places with high amounts of human movement
Retail Analytics (https://maxerience.com/how-image-recognition-is-enhancing-retail-experiences-with-object-detection/):
- Tracking customer movements to understand shopping patterns and optimize store layouts.
- Ensuring shelves are stocked by tracking inventory movement and predicting restocking needs.
Sports and Entertainment (https://www.rokoko.com/insights/motion-capture-in-sport):
- Tracking players during games or practices to gather performance statistics
- Tracking objects such as the ball in order to automate certain refereeing/umpiring decisions
- Analyzing player movements and strategies in sports broadcasts
Autonomous Vehicles (https://web.stanford.edu/class/cs231a/prev_projects_2016/object-detection-autonomous.pdf
):
- Ensuring autonomous vehicles can track and avoid obstacles, including pedestrians and other
- Assisting in the navigation of autonomous systems by tracking landmarks and other vehicles
Healthcare (https://research.aimultiple.com/computer-vision-healthcare/)
- Tracking patient movements in hospitals to ensure safety and improve care
- Assisting in minimally invasive surgeries by tracking surgical instruments and anatomy in real-time
Technical Application:
In this example, we explore the application of the YOLOv8 model from Ultralytics combined with ByteTrack for object tracking. YOLOv8, renowned for its high performance and versatility, is available in five sizes – nano, small, medium, large, and extra large, each designed to balance precision and computational efficiency. For our project, we opted for the extra large model to achieve maximum performance in terms of accuracy and detection capabilities. ByteTrack, recognized for its superior multi-object tracking capabilities, was employed to enhance the continuity and reliability of the tracking process. The combination of YOLOv8 for detection and ByteTrack for tracking aims to deliver precise and consistent identification and monitoring of these moving objects across sequential frames.
For our example, we chose to test the viability of YOLOv8 and ByteTrack when applied to track a singular object as well as multiple objects. Therefore, we ran the model on a video of a singular person walking as well as a video of several cars driving along a road.
Below, we can see the results of running the model on the first video:
Here, we can see that the model has detected the person in the first frame. It has labeled the person with a proper classification, a confidence of 0.95 (model is 95% confident that this is a person), both of which are also steps in object detection. However, the model has also given the person an identification number (#1) as part of the label. Looking at the second frame, we can see that the model still detects the person. However, the confidence has decreased to 0.92, which can potentially be explained by the fact that the person is farther away from the camera. However, we can see that the identification number of the person is still the same, meaning our model has correctly identified this person to be the same person from the previous frame, and tracked their movement. We can also see the model’s effectiveness on videos with multiple objects below:
Here, we can see that the model has identified every vehicle on the road in the first frame. We can even see that it has incorrectly identified object #105 as a truck instead of a car, likely due to it being too far from the camera to properly classify. In the second frame, we can see that the model has continued to identify every vehicle with the ID numbers staying consistent with the first frame. We can even see that object #105 has now been properly classified as a car.
Conclusion:
Object tracking is a pivotal advancement in computer vision, extending beyond object detection by maintaining the identity of detected objects across multiple frames, enabling the analysis of their movement and behavior over time. Unlike object detection, which identifies and locates objects within individual frames, object tracking ensures continuity, crucial for understanding dynamic scenes. This capability is essential for applications requiring a temporal understanding of object trajectories, such as in retail analytics, sports analysis, autonomous vehicles, and healthcare.
The integration of YOLOv8 for object detection with ByteTrack for object tracking exemplifies the synergy between detection and tracking technologies. This combination effectively demonstrates enhanced precision and reliability, as seen in our experiments tracking both a single person and multiple vehicles. The robust performance of these models highlights their potential to transform various industries, offering deeper insights and automating complex tasks. As technology evolves, object tracking will become increasingly integral to computer vision applications, driving innovation and efficiency across diverse fields.