Object Tracking: a technology at the heart of automated vision
Object tracking, or object tracking, is an important technique in the field of computer vision, making it possible to track the position and movements of an object in a sequence of images or videos. Thanks to the advances in artificial intelligence, this technology has seen significant progress, especially with the use of deep neural networks. These models not only allow to track objects accurately, but also to manage complex environments where objects can move quickly, change shape, or be temporarily hidden.
In addition, it is important to note that using this technique requires both the use of the right artificial intelligence algorithms, but also the use of labelled data accurately to improve (and measure) the performance of systems of Tracking. By combining AI with object tracking algorithms, it is becoming possible to carry out follow-ups of unprecedented reliability and precision, which promises increasingly efficient applications, especially in the field of real-time video analysis!
🧐 Curious to know more about Object Tracking ? We tell you everything in this article!
What is object tracking?
Object Tracking, or object tracking, is a computer vision task that involves tracking the position of a specific object over time in a sequence of images or videos. Unlike the simple object detection, which identifies their location in a single image, the Tracking of objects follows this object through several images, thus making it possible to capture its movement and its interactions with the environment.
The Object Tracking process is based on several key steps. First, the object must be detected in an image or video using a detection algorithm. Once identified, the algorithm of Tracking assign an ID to this object to track it through the following images.
Next, the algorithm predicts the future position of the object taking into account its past movements and the characteristics of its environment. It constantly adjusts its motion prediction as the object moves, even when there are variations such as a change in angle, shape, or the appearance of obstacles (occlusion). To ensure accurate tracking, it is necessary that tracking information be updated correctly, especially when the appearance of the object changes or when it is temporarily hidden.
What are the main object tracking algorithms used today?
Today, several algorithms are commonly used to track and analyze objects in the field of computer vision. These algorithms, including deep neural networks, vary in terms of precision, speed, and ability to handle complex situations such as occlusion or rapid changes in the object being monitored. It is essential to use the latest version of algorithms or models to improve object tracking performance.
Here are the main algorithms currently in use:
KCF (Kernelized Correlation Filter)
This algorithm uses correlation filters to track objects in real time with low resource consumption. It is fast and effective for tracking objects in relatively stable environments, but may be less effective in the event of occlusion or drastic changes in the appearance of the object.
MOSS (Minimum Output Sum of Squared Error)
MOSSE is a very fast tracking algorithm that uses correlation filters based on the optimization of squared errors. It is suitable for real-time applications where speed takes precedence over absolute precision. However, its robustness may be limited in complex environments.
CSRT (Discriminative Correlation Filter with Channel and Spatial Reliability)
CSRT is an improvement of algorithms based on correlation filters, like KCF. It takes into account spatial reliability and channel discrimination for more accurate monitoring. Although it is slower than KCF, it better handles situations where the appearance of the object changes or where occlusion occurs.
MedianFlow
This algorithm focuses on tracking objects by evaluating the trajectories between images. It is very good for slow and predictable movements and is capable of detecting tracking errors, but it is less suitable for fast movements or objects undergoing significant transformations.
TLD (Tracking, Learning, and Detection)
The TLD combines monitoring with continuous learning and object detection. He is able to re-learn an object if it disappears temporarily from the field of vision or changes its appearance. This flexibility makes it a powerful algorithm for tracking objects in dynamic environments, but it can be slower than other methods.
DeepSort (Simple Online and Realtime Tracking with a Deep Association Metric)
This algorithm combines real-time object tracking with characteristics extracted using deep neural networks. It is particularly effective for multi-object tracking in complex scenes and for cases where objects follow unpredictable trajectories. It is often used with object detection networks like YOLO or Faster R-CNN.
Siamese Networks (SiamRPN, SiamMask)
Siamese networks, like SiamRPN and SiamMask, use convolutional neural networks to make matches between an object model and the following images, thus facilitating tracking. These algorithms offer a balance between speed and precision, and are robust to changes in the appearance of the object.
Kalman Filter
The Kalman filter is a probabilistic algorithm that predicts the future position of an object based on its past and current state. It is widely used in systems where objects move in a predictable manner. Although it is very effective for linear or slightly noisy movements, it can have trouble following non-linear or erratic movements.
Particle Filter (Condensation algorithm)
This algorithm uses a series of particles to estimate the position of an object taking into account uncertainties in its movement. The particulate filter is more flexible than the Kalman filter and can handle more complex and non-linear movements. However, it is more expensive in terms of calculation.
Optical Flow
Optical flow is a method that tracks objects by analyzing pixel movements between images. It is particularly useful for tracking objects that are deformable or rapidly changing shape, but it can be sensitive to changes in lighting and is computationally expensive for large images.
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Why is data annotation essential for tracking objects in AI?
Let's go back to our core business, namely the preparation of datasets to feed the pipelines artificial intelligence, otherwise called ”data annotation”. It is an essential component of object tracking in artificial intelligence (AI) because creating metadata (in other words, adding a semantic layer to raw data) plays a fundamental role in training computer vision models.
Here's why data annotation is so important in this field:
1. Supervised model training
AI-based object tracking typically relies on supervised models, which require large amounts of labeled data to learn how to recognize, detect, and track specific objects in a video or image sequence. These annotations provide information about the position, class, and sometimes appearance of objects in each image. Without properly annotated data, AI models can't learn to distinguish objects, compromising their ability to track objects accurately.
2. Delineation of the objects to be monitored
Data annotation makes it possible to clearly define encompassing boxes (Bounding Boxes) around the objects to be followed. These boundaries allow the algorithm toObject tracking to understand where an object starts and ends. In some cases, more advanced annotations like segmentation masks are used to identify the exact contours of the object, which is essential for accurate tracking, especially in complex environments.
3. Improving model accuracy
Annotated data provides a basis upon which the model is constantly adjusting its predictions and parameters. The more accurate and varied the annotations, the more the model is able to track objects in diverse environments, taking into account changes in scale, angle, occlusion, or deformation. In contrast, poorly annotated or incomplete data can lead to biased or inaccurate models.
4. Complex scenario management
Annotation makes it possible to capture complex and difficult real-world scenarios, such as occlusion (when the object is temporarily hidden), rapid movements, partially visible objects, or interactions between several objects. These annotations are essential to train algorithms to correctly predict the trajectory of an object, even when it disappears temporarily from the field of vision.
5. Facilitating multi-object tracking
In cases where multiple objects need to be monitored simultaneously, data annotation becomes even more important. The models of object tracking multi-objects depend on the correct assignment of unique identifiers to each object, so that they can be followed individually throughout the sequence. Appropriate annotations make it possible to disassociate objects and avoid confusion between them, especially when they interact or overlap.
6. Enrichment of models thanks to the diversity of data
AI models require diversified data in order to generalize well. Annotations help enrich this data by including different types of objects, from various angles, with variations in light, movement, and in various environments. This allows the models to be more robust and to better adapt to real conditions during deployment.
7. Validation and performance evaluation
Finally, annotation is also essential in the validation and evaluation phases of AI models. Annotated data makes it possible to measure the accuracy of tracking algorithms by comparing their predictions to reality. This helps to detect errors, adjust settings, and improve model performance before they are used in production. Appropriate validation and evaluation are required to ensure the success of object tracking, ensuring accurate and reliable results.
Conclusion
Object tracking, reinforced by the capabilities of artificial intelligence, is now an essential tool in the field of computer vision. Thanks to ever more efficient algorithms and high-quality datasets (i.e., images and videos enriched with semantic labels), object tracking systems can operate in complex environments and meet precision requirements in real time.
This technique has applications in sectors as diverse as security, robotics, autonomous vehicles or even sports analysis. With the continuous evolution of annotation techniques and deep learning methods, object tracking algorithms continue to gain in robustness and flexibility, making automated vision more reliable than ever. Also, as access to data resources improves, these innovations promise to further transform the way we interact with the visual world!
