# Improved DeepSORT Algorithm Based on Multi-Feature Fusion
## 1 Introduction  
With the great breakthroughs in deep learning in computer vision in recent years, deep learning has been used for pedestrian multi-target tracking, and improving the accuracy of target tracking is the mainstream of multi-target tracking research [1]. The key steps of multi-target tracking algorithms are target apparent feature extraction, calculation of appearance similarity measure or distance measure between the newly detected target and the target in the trajectory, and prediction of motion trajectory [2]. At present, the most mature research direction of deep learning technology in the field of pedestrian multi-target tracking is the extraction of the apparent features of tracked pedestrians [3].  
AlexNet Network, proposed by [PERSON] [4], was first convolutional network applied to image feature extraction and won the ImageNet competition that year, prompting people to enter the era of deep feature extraction. [PERSON] et al. [5] proposed a SORT algorithm, which brought deep learning into multi-target tracking for the first time. This method mainly used Faster Region Convolutional Neural Networks (R-CNN) [6] algorithm based on deep learning to detect pedestrians and get the pedestrian's position in the current video sequence. By using a deep learning-based detector, a Kalman filter combined with the detection algorithm gave the current state information for the target and the prediction information of the previous state in order to estimate the current state of the target. Then, the weight was assigned through the intersection union ratio (IOU) distance, the data were correlated through Hungarian calculation, and the detected results were assigned to the estimated trajectory. The performance of this method possessed high-speed accuracy. At the same time, the algorithm achieved a tracking speed of 60 Hz. [PERSON] et al. proposed DeepSORT [7], which adds a pedestrian appearance feature similarity measure based on the SORT algorithm and combines a cascade matching module to reduce the amount of ID switching when pedestrians are occluded, improving the robustness of the model. [PERSON] et al. embedded the feature extraction module of the multi-target tracker into the networkof YOLOv3, which jointly learns the detector and embedding model (JDE) [8]. During the training process, the weights are directly shared with the detection network so that the tracker can use the target border output by the detector and the corresponding appearance features. This method improved the real-time tracking performance. [PERSON] et al. proposed the FairMOT detection algorithm [9] based on the idea of JDE because JDE directly used the feature extraction network module of the YOLOv3 [10] algorithm, which led to the existence of anchor frames. Meanwhile, the detection network and tracking algorithm have different features for pedestrian appearance, which makes them less accurate in dealing with dense pedestrians. FairMOT network uses the anchorless-frame design of CenterNet [11]. At the same time, downsampling was used to extract pedestrian appearances, which greatly improves tracking accuracy. In summary, it can be seen that improving the accuracy of pedestrian appearance feature extraction is of great significance to improve the accuracy of pedestrian multi-target tracking.  
[PERSON] et al. [12] combined FPN and ResNet to extract and fuse image features, which enhanced the semantic information and contour of occluded pedestrians and improved the performance of pedestrian detection. [PERSON] et al. [13] compared the impact of different data-association measures on the tracking effect of the DeepSORT algorithm; it can be seen that better data-association measures can improve the tracking effect of tracking algorithms.  
Based on the above results, this paper improved the feature extraction network model in the DeepSORT algorithm and introduced a new data-association measure to improve the tracking accuracy of the algorithm.