FairMOT: A Model for Multi-Object Tracking
FairMOT is an innovative model designed to track multiple objects accurately using two homogeneous branches to predict pixel-wise objectness scores and re-ID features. The model's main objective is to ensure fairness between the tasks and ultimately achieve high levels of tracking and detection accuracy.
The detection branch estimates object centers and sizes by using position-aware measurement maps in an anchor-free style. This differs from other methods that perform detection and re-ID in a cascaded style. FairMOT's innovative approach helps to align re-identification (re-ID) features to object centers, leading to significant improvements in tracking accuracy compared to previous methods.
How FairMOT Works
FairMOT uses two homogeneous branches that work together to track objects. The first branch uses an anchor-free style to detect and estimate the position and size of objects accurately. The second branch estimates re-ID features for each pixel to characterize the object centered at the pixel. Both branches work simultaneously to maintain fairness and achieve high levels of accuracy.
The detection branch uses position-aware measurement maps to estimate object centers and sizes. This is achieved without the use of anchors, which is different from previous methods of object detection. Eliminating anchors helps to align features better by reducing the gap between object center and re-ID features. This leads to improved object detection and tracking accuracy.
The re-ID branch estimates re-ID features for each pixel to characterize the object centered at the pixel. By using this approach, FairMOT can identify an object in a video sequence based on its appearance consistently. This feature is particularly useful when tracking multiple objects in a video sequence.
The Advantages of FairMOT
FairMOT offers several advantages over other object tracking models currently available. The two homogeneous branches work in tandem to achieve high levels of detection accuracy and tracking accuracy for multiple objects.
One of the primary advantages of using FairMOT is the elimination of anchors in the detection branch. This leads to the alignment of re-ID features with object centers, which significantly improves tracking accuracy.
FairMOT also uses high-resolution feature maps of strides four, which is different from previous anchor-based models that use feature maps of stride 32. The use of high-resolution feature maps, combined with an anchor-free style of detection, improves object detection and tracking accuracy significantly.
FairMOT is an innovative model for multi-object tracking that can accurately detect and track objects. The model achieves fairness between tasks by using two homogeneous branches that work together to predict pixel-wise objectness scores and re-ID features.
The use of an anchor-free style of detection, combined with high-resolution feature maps of strides four, aligns features with object centers, which significantly improves tracking accuracy. This model offers several advantages over previous methods of object tracking, making it a valuable tool for various applications that involve tracking multiple objects simultaneously.
