# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
## 3 Problem Formulation and the Approach
### The Architecture of the Proposed MOT Framework  
We propose an effectively finite-tailed updating (FTU) strategy to generate the appearance template, which exploits the local temporal context of the object in videos and dynamically combines them to enhance the feature representation of the object. Figure 3 is the overview architecture of our MOT framework. The \(T_{i}\) here represents the i-th frame to be input into the backbone network. The input frames are obtained directly without any processing. We first input the \(T_{0}\) frame into the network and then obtain the motion and appearance features of all objects in the frame. Then, the \(T_{i}\) frame is input into the network to obtain the motion and appearance features of all objects. Finally, the current frame is input into the network to obtain the motion and appearance features of all objects. We use the cosine distance to calculate the similarity between the appearance features of all the objects in the \(T_{i-1}\) frame and the appearance features of all the objects in the \(T_{i}\) frame. The Mahalanobis distance is used to calculate the similarity between the movement features of all objects in the \(T_{i-1}\) frame and the movement features of all objects in the \(T_{i}\) frame. We use a hyperparameter \(\lambda\) to combine the cosine distance and Mahalanobis distance to obtain the cost matrix. We use the Hungarian algorithm to match all the objects in the \(T_{i-1}\) frame and the \(T_{i}\) frame. If the object is successfully matched, in order to obtain better appearance and movement features, we need to update the appearance and movement features to adapt to the changes in the appearance and movement of the object in different frames.  
Figure 2: The detailed information of the appearance template update mechanism of the current method and the appearance template update mechanism of our proposed method. \(\lambda\), \(\gamma\), and \(\mu_{i}\) are hyperparameters.  
The updating methods of all the object motion models are as follows. We use the Kalman filter to predict the movement features of objects in the \(T_{i}\) frame through the actual movement features of objects in the \(T_{i-1}\) frame, and then update the actual movement features of objects in the \(T_{i}\) frame by combining the movement features measured in the \(T_{i}\) frame with the movement features predicted in the \(T_{i-1}\) frame. Then, the movement features of the object in the current frame are predicted by the actual movement features of the object in the \(T_{i}\) frame. Finally, the actual movement features of the object in the current frame are updated by combining the predicted movement features of the object in the \(T_{i}\) frame with the measured movement features of the object in the current frame.  
The updating method of the appearance features of objects is as follows. Firstly, we input the \(T_{i-1}\) frame into the network, and then combine it with the detection box of the object obtained by the backbone network. We intercept all the objects in the frame and then extract the appearance features of all the objects in the \(T_{i-1}\) frame through the Re-ID feature extraction network. Then, the \(T_{i}\) frame is input into the backbone network, we repeat the process of the \(T_{i-1}\) frame, and extract the appearance features of all the objects in the \(T_{i}\) frame. Finally, we dynamically combine the historical accumulated appearance templates of objects in multiple frames and the Re-ID features of the current frame to update the actual appearance template of the current frame.