# Improved DeepSORT Algorithm Based on Multi-Feature Fusion
## 5 Experimental Results and Analysis  
Firstly, the improved feature extraction model was trained using the official and public Market-1501 pedestrian recognition dataset, which is composed of six cameras to collect 1501 pedestrian data points in a school. A total of 32,668 pedestrian pictures are collected, of which 12,936 make up the training set and 19,732 the test set. Each pedestrian image has a different angle and camera acquisition. Some of the data are shown in Figure 10 below. We use all the training sets of Market-1501 to train our feature extraction network, and use the first picture of each pedestrian in the Market-1501 test set as our test set. A total of 80 rounds of training with 64 samples in each batch are performed, as shown in Figure 11.  
Figure 11: Feature extraction network training results. **(a)** Training Loss. **(b)** Top1 Error. Figure 10: Pedestrian recognition partial dataset. Figure 11a depicts the loss curve during feature extraction network training and prediction, and Figure 11b represents the Top-1 error rate change during feature extraction network training and prediction, where the blue segment represents the training phase and the red segment represents the prediction phase. As you can see from Figure 11a, in the first 20 th epoch of training, the loss decreases very quickly; it slows down gradually thereafter, accelerates suddenly in the 40 th epoch, flattens out until training loss in the 70 th epoch stabilizes, and then ends at the 80 th epoch. In the prediction stage, the downward trend of loss is similar to that in the training phase, but the first 40 th epoch of training experiences severe fluctuations due to inadequate model accuracy at the beginning of training. In Figure 11b, the curve change law of the Top-1 error rate is similar to that of the loss curve in Figure 11a, and the training is also completed in the 80 th epoch. After training, the training results are imported into the DeepSORT tracking module to verify the performance of the improved pedestrian multi-target tracking algorithm on the MOT-16 dataset. Our algorithm is the matching algorithm without replacing IOU, and Our-1 is the algorithm using GIOU to replace IOU. The evaluation index False Positive (FP) is the number of false detections, False Negative (FN) is the number of missed detections, Identity Switch (IDS) indicates the number of identity exchanges of all tracking targets. Multi Objective Tracking Accuracy (MOTA) is expressed by the following equation:  
\[\text{MOTA}=1-\frac{\sum\left(\text{FP}+\text{FN}+\text{IDS}\right)}{\sum \text{GT}} \tag{9}\]  
In Equation (9) above, GT represents the number of tracking targets. The Multiple Object Tracking Precision (MOTP) measures the positioning accuracy of the detector, which is expressed by the following equation:  
\[\text{MOTP}=\frac{\sum_{t,i}d_{t,i}}{\sum_{t}c_{t}} \tag{10}\]  
In Equation (10), \(c_{t}\) represents the total number of matches at frame \(t\), and \(d_{t,i}\) represents the distance between the hypothetical bounding box and the real bounding box. The verification results are shown in Table 1.  
From Table 1, it can be seen that MOTA and MOTP in the SORT algorithm without the feature extraction network are much lower than the DeepSORT pedestrian tracking algorithm with the feature extraction network, and the number of IDS is much higher than the DeepSORT pedestrian tracking algorithm. It can be seen that the feature extraction network has a great impact on improving tracking. Compared with the DeepSORT algorithm, the number of FP in Our algorithm in the table is reduced by 1106, an 8.6% decrease. The number of FN is reduced by 8286, a decrease of 22.5%, which is the best evaluation parameter to improve. The number of IDS has been reduced by 132 (16.9%). The final tracking accuracy (MOTA) of Our tracking algorithm is increased by 2.3 compared with the DeepSORT algorithm. The relative improvement is 3.7%, but the tracking accuracy (MOTP) is decreased by 0.25%, reduced by 0.2.