Overview of Various Tracking API Methods
In this article, I am going to discuss the Overview of Various Tracking API Methods. Please read our previous article where we discussed MeanShift and CamShift Tracking with OpenCV.
Overview of Various Tracking API Methods
Many computer vision libraries include high-level Tracking API methods to make the construction of Object Tracking algorithms easier. These APIs provide developers with pre-implemented algorithms and routines that manage the intricacies of object tracking, enabling them to focus on their individual application needs.
OpenCV, one of the most popular computer vision libraries, has a complete Tracking API with multiple tracking methods. These techniques are easily accessible and used in numerous programming languages like Python and C++ thanks to the OpenCV package.
Tracking Methods Available in OpenCV
Among the most prominent tracking methods available in OpenCV are:
BOOSTING Tracker: An AdaBoost-based real-time object tracking method. It performs well in real-time tracking conditions but may suffer from drifting in other cases.
MIL (Multiple Instance Learning) Tracker: This tracker employs the notion of multiple instance learning, where a positive example is a bag containing instances of the target object and a negative example is a bag having no instances of the target object. It produces reliable tracking data but is likely to be slower than other algorithms.
KCF (Kernelized Correlation Filters) Tracker: The KCF tracker tracks objects using the notion of correlation filters. It performs well in terms of precision and speed, making it suited for real-time applications.
TLD stands for Tracking, Learning, and Detection. Tracker: The TLD tracker integrates approaches for tracking, learning, and detection. It is capable of dealing with occlusions, scale changes, and sudden movements. However, in complicated settings, it may yield false positives.
These are only a few of the tracking algorithms provided by the OpenCV Tracking API. Developers can select the best algorithm for their needs based on the parameters of their tracking situation.
Object Tracking API for TensorFlow
TensorFlow, a prominent deep learning framework, has an Object Tracking API that uses neural networks to monitor things. The TensorFlow Object Tracking API, which is developed on top of the TensorFlow framework, offers pre-trained Object Tracking models that may be fine-tuned or utilized immediately.
TensorFlow’s Object Tracking API supports the following methods:
DeepSORT (Deep Learning-based SORT): DeepSORT blends deep learning ideas with the well-known SORT (Simple Online and Realtime Tracking) method. It has strong multi-object tracking capabilities and is especially useful in busy environments.
IOU (Intersection over Union) Tracker: This basic tracker calculates the resemblance between bounding boxes in successive frames using the notion of IoU. It is computationally efficient and performs well in non-overlapping object settings.
Centroid Tracker: A simple algorithm that tracks objects based on their centroids is the Centroid Tracker. It is appropriate for situations in which items have discrete locations and do not overlap.
The TensorFlow Object Tracking API’s tracking methods enable developers to harness the power of deep learning for precise and efficient object tracking.
Tracking API for PyTorch
Another prominent deep learning toolkit, PyTorch, includes a Tracking API with pre-trained Object Tracking models and algorithms. This API makes it easier to build object tracking in PyTorch.
The PyTorch Tracking API supports the following features:
FairMOT (Fair Multi-Object Tracking): FairMOT is a multi-object tracking method based on deep learning that combines the capabilities of object recognition and tracking. It achieves cutting-edge performance on a variety of Object Tracking benchmarks.
SiamRPN (Siamese Region Proposal Network): SiamRPN is an object tracking technique that employs a Siamese network design. It has exceptional tracking accuracy and operates in real time.
These methods of the PyTorch Tracking API allow developers to take advantage of deep learning capabilities for accurate and sophisticated object tracking.
Object tracking is an important task in computer vision, and the Tracking API methods supplied by prominent computer vision libraries make it easier to construct tracking algorithms. Libraries like as OpenCV, TensorFlow, and PyTorch provide a variety of pre-implemented tracking methods, each with its own set of advantages and disadvantages. These APIs enable developers to select the best appropriate tracking technique depending on their individual requirements and the features of their tracking scenarios.
Developers can save time and effort in building Object Tracking algorithms from scratch by utilizing the Tracking API methods. They may concentrate on fine-tuning and modifying the offered algorithms to their individual use cases, allowing for precise and efficient object tracking in a variety of computer vision applications.
In the next article, I am going to discuss Deep Learning for Computer Vision. Here, in this article, I try to explain the Overview of Various Tracking API Methods. I hope you enjoy this Overview of Various Tracking API Methods article. Please post your feedback, suggestions, and questions about this article.