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15 December 2022

Person Re-Identification with Improved Performance by Incorporating Focal Tversky Loss in AGW Baseline

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Department of Electrical Engineering, National Taiwan Normal University, Taipei 106, Taiwan
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Sensors2022, 22(24), 9852;https://doi.org/10.3390/s22249852
This article belongs to the Special Issue Person Re-Identification Based on Computer Vision
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Abstract

Person re-identification (re-ID) is one of the essential tasks for modern visual intelligent systems to identify a person from images or videos captured at different times, viewpoints, and spatial positions. In fact, it is easy to make an incorrect estimate for person re-ID in the presence of illumination change, low resolution, and pose differences. To provide a robust and accurate prediction, machine learning techniques are extensively used nowadays. However, learning-based approaches often face difficulties in data imbalance and distinguishing a person from others having strong appearance similarity. To improve the overall re-ID performance, false positives and false negatives should be part of the integral factors in the design of the loss function. In this work, we refine the well-known AGW baseline by incorporating a focal Tversky loss to address the data imbalance issue and facilitate the model to learn effectively from the hard examples. Experimental results show that the proposed re-ID method reaches rank-1 accuracy of 96.2% (with mAP: 94.5) and rank-1 accuracy of 93% (with mAP: 91.4) on Market1501 and DukeMTMC datasets, respectively, outperforming the state-of-the-art approaches.

1. Introduction

Person re-identification [1,2,3] has become one of the most important computer vision techniques for data retrieval over the past years and is commonly used in vision-based surveillance systems equipped with multiple cameras, each having a unique viewpoint non-overlapped by others [4,5]. When an image is captured by a camera, the person of interest can be located by utilizing object detection methods, such as YOLO [6], RCNN [7], and SSD [8]. Given the person’s image as a query, person re-identification is applied to measure the similarity between the query image and the images in a gallery to generate a similarity ranked list ordered from the highest to the lowest one. To fulfill this task, it is necessary to provide robust modeling for the body appearance of a person of interest rather than relying on biometric cues (human faces) [9]. This is because the captured image may not always be the frontal view of the person. Traditional person re-identification approaches generally focus on gathering information from color [10] and local feature descriptors [11]. However, it is not easy to model complicated scenarios through such low-level features. Thanks to the advance of GPU computational capability and machine learning techniques, the trend of person re-identification has turned into learning-based approaches [2,12,13,14,15,16,17] that can make predictions better than humans [14]. However, existing person re-identification approaches encounter prediction difficulties in different viewpoints, illumination changes, unconstrained poses of the person, poor image quality, appearance similarity among different persons, and occlusion. Hence, person re-ID is still a challenging issue in the field of computer vision.
Typically, person re-ID systems can be separated into three components, including feature representation learning, deep metric learning, and ranking optimization. First, feature representation learning determines the choice of input data format and the architecture design. The former searches for an effective design between using single-modality and heterogeneous data [18,19,20]; the latter [21,22,23,24] focuses on the model backbone construction for generating features that maximize the Euclidian distance of features between different persons and minimizes the distance of features targeting the same person. In the early years, the popular research trend has been to use the global features of the person of interest, such as the ID-discriminative embedding model [25]. Then, several widely-used approaches have proven the benefits of using local features or features from meaningful body parts [12,26,27]. Next, deep metric learning focuses on the training loss design and sampling strategy, which will be introduced in more detail in Section 2.3. Last, ranking optimization dedicates itself to improving the initial ranked list by revising or remapping the similarity scores via various algorithms. Liu et al. [28] proposed a one-shot negative feedback selection strategy to resolve the inherent visual ambiguities. Ma et al. [29] designed an adaptive re-ranking query strategy with respect to the local geometry structure of the data manifold to boost the identification accuracy. Zhong et al. [30] ensured a true match in the initial ranked list according to the similarity of the gallery image and the probe in k-reciprocal nearest neighbor. However, the enhancement of ranking optimization highly depends on the quality of the initial ranked list. In other words, an advanced design of feature representation learning and deep metric learning is still needed.
In this work, we aim to enhance the deep metric learning with a more effective design. Inspired by a vital concept of many effective loss designs using a combination of multiple types of loss function, we investigate and decide to incorporate a focal Tversky loss in the AGW [2] baseline. Nevertheless, the support of feature representation and re-ranking are also considered in our re-ID design. Different from the original setting of using ResNet [31] as the model backbone, ResNeSt50 [32] is used in the proposed method to obtain a better feature representation of the person of interest. Besides, a re-ranking technique is also applied to make a final lift in re-ID performance. To this end, the contribution of this work can be summarized as follows:
  • We propose a novel training loss design for incorporation into the AGW baseline in the training process to enhance the prediction accuracy of person re-identification. To the best of our knowledge, this work is the first to incorporate a focal Tversky loss in deep metric learning design for person re-identification.
  • Different from the original AGW, a re-ranking technique is applied in the proposed method to give a boost to improve the person re-identification performance in the inference mode.
  • The proposed method does not require additional training data, and it is easy to implement on ResNet, ResNet-ibn [33], and ResNeSt backbones. Moreover, the proposed method achieves state-of-the-art performance on the well-known person re-identification datasets, Market1501 [34] and DukeMTMC [35]. Besides, we investigate the receiver operating characteristic (ROC) performance among the above three backbones to verify the sensitivity and specificity among various thresholds.
The rest of the paper is organized as follows: Section 2 introduces the related works; Section 3 presents the proposed method; Section 4 shows the training detail and experimental results; Section 5 contains a discussion that highlights the main observations and several open issues for further research, and a conclusion is given in Section 6.

3. Method

The framework of the proposed method is shown in Figure 1. When an input query image is fed into the system, the image is pre-processed and fed into the ResNeSt Backbone structure pre-trained by ImageNet [48]. Then, a loss computation module is introduced to obtain ID loss in the training process. On the other hand, the process in the inference mode is exactly the same, except that a re-ranking optimization is applied after the initial ID list is generated. Note that the proposed method is built on top of the AGW baseline.
Figure 1. Framework of the proposed method.

3.1. Feature Generator

In the pre-processing module, the input image is resized to a uniform scale of 256 × 128 pixels. We then normalize the RGB channel of the image with a mean (0.485, 0.456, 0.406) and standard deviation (0.229, 0.224, 0.225), following the settings of ImageNet [47]. Subsequently, we zero pad 10 pixels on the borders of each image before taking a random crop of size 256 × 128 pixels. After that, these cropped images will be randomly sampled to compose training batches. Different from the AGW baseline, we replace the ResNet50 backbone with the ResNeSt50 backbone, which contains a split attention block as shown in Figure 2. The advantage of using ResNeSt block is that it can extract individual salient attributes and hence provide a better image representation. In the setting of this work, the radix, cardinality, and width attributes of ResNeSt block are set to 2, 1, and 64, respectively. In the final stage, the data will be aggregated by generalized mean pooling (GeM) followed by batch normalization for extracting more domain-specific discriminative features that correspond to the important key points of the input image.
Figure 2. ResNeSt block [32] © 2022 IEEE.

3.2. Loss Computation

The proposed loss computation is shown in Figure 3, where the generated features are fed into a fully connected (FC) layer to make ID prediction after the process of the feature generator. The prediction result will then be used to calculate three loss functions, including cross entropy, triplet loss, and focal Tversky loss. While the original AGW only considers the former two loss functions, the proposed method adds a focal Tversky loss that has the advantage of addressing the issue of data imbalance and facilitating the model to learn effectively in a small region of interest [48]. The focal Tversky loss LFT is defined as:
LFT = (1 − LT)γ,
where
LT = TP/(TP + α∙FN + β∙FP).
Figure 3. The proposed loss computation.
TP, FN, and FP indicate true positive, false negative, and false positive numbers of the prediction, respectively. α, β, and γ are adjustable parameters. We manually select a set of pre-determined values for the parameters in this work. The final loss design is a combination of the focal Tversky loss, triplet loss, and cross entropy loss:
Lfinal = LFT + LCE + LTR

3.3. Re-Ranking Optimization

In the proposed method, the re-ranking optimization is used in the inference step to enhance the accuracy of the final prediction of person re-identification. Considered as a post-processing tool, the re-ranking with k-reciprocal encoding [30] is applied after the initial ID list is generated as shown in Figure 4. The reason for using re-ranking in the proposed method is that it helps to enable a more accurate prediction in person re-ID. Besides, it is acceptable for data retrieval to execute in offline mode. The parameter setting is the same as that of the original paper. As the initial ranked list is generated, the top-k samples of the ranked list are encoded as reciprocal neighbor features and utilized to obtain k-reciprocal features. Then, the Jaccard distance is calculated with the k-reciprocal features of both images. Next, the Manhalanobis distance of feature appearance aggregates with the Jaccard distance to obtain the final distance. Finally, the initial ranked list is revised according to the final distance.
Figure 4. Re-ranking strategy [30] © 2022 IEEE.

4. Experimental Results

To evaluate the proposed person re-ID system, we conducted our experiments on an Intel (R) Core (TM) i7-7700 @ 3.6 GHz and an NVIDIA GeForce RTX 3090 graphic card. The well-known Market1501 and DukeMTMC datasets were used to evaluate the performance of the proposed method against state-of-the-art approaches. Market1501 is a dataset for person re-identification, wherein six cameras were placed in an open-system environment to capture images. It targets 1501 identities and contains a total of 32,668 + 500 K annotated bounding boxes and 3368 query images. DukeMTMC is a dataset focusing on 2700 identities, which contains more than 2 million frames using eight cameras deployed on the Duke University campus. Note that the Adam method was adopted to optimize the model, and the training epoch number was set to 200. The parameters (α, β, and γ) of the focal Tversky loss were manually determined to be (0.7, 0.3, 0.75) and (0.7, 0.3, 0.95) to train on the Market1501 and DukeMTMC datasets, respectively. For easy reference, the hyperparameters of the proposed method are summarized in Table 1.
Table 1. Hyperparameters of the proposed method.
In the first experiment, we compare the performance of person re-identification with several state-of-the-art approaches, including PCB [36], BoT [41], SCSN [42], AGW [2], and FlipReID [43]. The evaluation metrics are rank-1 accuracy (R1), mean of average precision (mAP), and mean inverse negative penalty (mINP) [2]. The comparison of the re-ID performance is shown in Table 2, where we can see that the proposed method with the ResNeSt50 backbone achieves state-of-the-art performance on both the Market1501 and DukeMTMC datasets. Although the mAP of FlipReID (mAP: 94.7) is slightly higher than that of the proposed method (mAP: 94.5) on the Market1501 dataset, the rank-1 accuracy of the proposed method (R1: 96.2) is superior to that of FlipReID (R1: 95.8). Moreover, compared with FlipReID, our method has the same rank-1 accuracy but higher mAP on the DukeMTMC dataset. Furthermore, we can see that the accuracy of the proposed method without re-ranking is still superior to the original AGW on both of the two datasets. This indicates that applying focal Tversky in deep metric learning does help boosting the prediction accuracy for person re-ID.
Table 2. Person re-ID performance comparison with state-of-the-art methods on the Market1501 and DukeMTMC datasets (Boldface indicates the best results).
Now, there is a question as to whether the validation of the proposed loss design comes directly and entirely from the superior backbone that we have chosen? Hence, it motivates us to investigate whether the loss design is still effective in boosting the person re-identification accuracy on the same backbone as the original AGW holds. We therefore conduct the same experiment on ResNet50 and ResNet50-ibn, and the results are listed in Table 3. We can see from Table 3 that the overall performance of the proposed method is still slightly better than the AGW baseline, even without the re-ranking process. Moreover, on the DukeMTMC dataset, the proposed method with ResNeSt50 backbone still holds first place compared to the other two backbone settings. However, on Market1501, the ResNet50-ibn with re-ranking holds the best performance on rank-1 and mINP. In fact, if the proposed method incorporates the re-ranking technique, the overall performance on Market1501 dataset is similar to each other among the ResNet50, ResNet50-ibn, and ResNeSt50 backbones, because there is no obvious difference between the scores of the proposed method without re-ranking among the three kinds of backbone. In other words, the re-ranking technique results in an almost identical boost in accuracy in person re-identification performance when the initial ranked list is similar.
Table 3. Person re-ID performance comparison with various backbone settings on the Market1501 and DukeMTMC datasets (Boldface indicates the best results).
The other metric for evaluating the performance of person re-identification is the ROC. As shown in Figure 5 and Figure 6, the vertical axis and horizontal axis of the ROC plot indicate the true positive rate and false positive rate, respectively. In this metric, it shows the classification performance on various threshold settings. The closer the curve becomes to near the top-left corner of the plot, the better the model performs in the prediction of the selected elements. As an attempt to compare the performance among different backbones in the same deep metric learning, we conducted an experiment on the two datasets by the proposed method with the three kinds of backbone settings. The ROC results on Market1501 and DukeMTMC are shown in Figure 5 and Figure 6, respectively. Note that “Ours-R50”,”Ours-R50-ibn”, and “Ours-S50” in the two figures indicate the proposed method using ResNet50, ResNet50-ibn, and ResNeSt50, respectively. Without using a linear scale, the horizontal axis of the ROC is plotted on a logarithmic scale instead for better clarity. In Figure 5, we can see that the three curves almost overlap with each other when the false positive rate is more than 10−3. On the other hand, if the false positive rate is less than 10, the model with ResNeSt50 is slightly closer to the top-left corner than the others. In Figure 6, the deviation is more obvious, but the model with ResNet50-ibn outperforms the other two backbones. Besides, the model with ResNeSt50 does not perform well in the ROC test even though it holds the highest accuracy on rank-1, mAP, and mINP metrics in Table 3. Through the above experiments, we have found that the performance of person re-ID in accuracy is not correlated with the performance in sensitivity and specificity.
Figure 5. ROC curve with the horizontal axis on a logarithmic scale on Market1501 dataset.
Figure 6. ROC curve with the horizontal axis on a logarithmic scale on DukeMTMC dataset.

5. Discussion

The experimental results have shown improved performance on the AGW baseline by incorporating focal Tversky loss in the proposed training loss. However, there is still room for improvement in this design. First, parameter tuning is one of the bottlenecks of this method. An optimal design of the three parameters (α, β, and γ) of focal Tversky loss for a particular closed-world dataset is not necessarily optimal in training on other datasets or the same dataset with additional virtual images generated by using data augmentation techniques. Besides, the tuning task demands a computational effort causing an extra cost in applying the method to larger datasets. Next, the re-ranking post-processing design limits the person re-identification method from working in real time. Although the re-ranking method with k-reciprocal neighbor used in this work is one of the most widely-used approaches, it is nevertheless challenging to seek an optimal solution that can balance both the accuracy and computational cost in an effective manner. Last, in the investigation of the ROC curve in Figure 5 and Figure 6, we can see that the method with the highest accuracy does not guarantee its performance in sensitivity (true positive rate) and specificity (false positive rate) among various thresholds. This phenomenon indicates that the re-ID model with the highest rank-1 accuracy may not be as accurate as it can be if it is applied to extract features on other datasets. As a result, it has to be carefully examined to apply the trained re-ID model to other open-world datasets. Nevertheless, it is our future research objective to eliminate the above drawbacks for better re-ID performance in terms of accuracy, speed, and robustness.

6. Conclusions

In this work, we have proposed a novel deep metric learning design that incorporates a focal Tversky loss in the AGW baseline and achieves an improved re-ID performance according to the experimental results. Due to the use of focal Tversky loss, the AGW re-ID baseline can address the data imbalance issue and learn effectively on the hard examples in the training process so as to improve the overall person re-ID accuracy. Besides, we have also evaluated the performance of the proposed method on various backbone settings in comparison with the original AGW baseline. Experimental results show that the overall performance of the proposed method is still better than the AGW baseline, even without the re-ranking process. On the other hand, by applying the re-ranking as a post-processing technique, the proposed method outperforms the state-of-the-art methods in rank-1 and mAP metrics on the Market1501 and DukeMTMC datasets. Moreover, an observation of the ROC curve in this work indicates that threshold settings should be carefully examined when applying the re-ID model to extract features, even if the model holds the highest rank-1 accuracy. The insight gained from this investigation is helpful for using the re-ID model as a feature extractor on open-world datasets.

Author Contributions

Conceptualization, S.-K.H. and C.-C.H.; methodology, S.-K.H.; software, S.-K.H.; validation, S.-K.H.; writing—original draft preparation, S.-K.H.; writing—review and editing; S.-K.H. and C.-C.H.; visualization, S.-K.H.; supervision, C.-C.H. and W.-Y.W.; project administration, C.-C.H.; and funding acquisition, C.-C.H. and W.-Y.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the “Chinese Language and Technology Center” of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan and the National Science and Technology Council (NSTC), Taiwan, under Grants no. 111-2221-E-003-025, 111-2222-E-003-001, 110-2221-E-003-020-MY2, and 110-2634-F-A49-004 under the Thematic Research Program to Cope with National Grand Challenges through Pervasive Artificial Intelligence Research (PAIR) Labs of the National Yang Ming Chiao Tung University. We are also grateful to the National Center for High-Performance Computing for computer time and facilities to conduct this research.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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