Multiple pedestrian tracking (MPT) has gained significant attention due to its huge potential in a commercial application. It aims to predict multiple pedestrian trajectories and maintain their identities, given a video sequence. In the past decade, due to the advancement in pedestrian detection algorithms, Tracking-by-Detection (TBD) based algorithms have achieved tremendous successes. TBD has become the most popular MPT framework, and it has been actively studied in the past decade. In this paper, we give a comprehensive survey of recent advances in TBD-based MPT algorithms. We systematically analyze the existing TBD-based algorithms and organize the survey into four major parts. At first, this survey draws a timeline to introduce the milestones of TBD-based works which briefly reviews the development of the existing TBD-based methods. Second, the main procedures of the TBD framework are summarized, and each stage in the procedure is described in detail. Afterward, this survey analyzes the performance of existing TBD-based algorithms on MOT challenge datasets and discusses the factors that affect tracking performance. Finally, open issues and future directions in the TBD framework are discussed.

Although deep learning-based face recognition techniques have achieved amazing performance in recent years, low-resolution (LR) face recognition remains challenging. In this letter, we address this problem by learning an identity-aware face super-resolution network to recover identity information of LR face. To learn identity-aware features efficiently, the identity features are explicitly disentangled to two orthogonal components: the magnitude and angle of features that project identity features to a hypersphere space. We show that the magnitude of features is related to the quality of a face. The proposed approach shows its superiority on recovering identity-related textures which are beneficial to recover identity information for recognition. Extensive experiments demonstrate the effectiveness of the proposed algorithm in LR face recognition.

Daytime visible modality (RGB) and night-time infrared (IR) modality person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. However, training a cross-modality ReID model requires plenty of cross-modality (visible-infrared) identity labels that are more expensive than single-modality person ReID. To alleviate this issue, this paper studies unsupervised domain adaptive visible infrared person re-identification (UDA-VI-ReID) task without the reliance on any cross-modality annotation. To transfer learned knowledge from the labelled visible source domain to the unlabelled visible-infrared target domain, we propose a Translation, Association and Augmentation (TAA) framework. Specifically, the modality translator is firstly utilized to transfer visible image to infrared image, formulating generated visible-infrared image pairs for cross-modality supervised training. A Robust Association and Mutual Learning (RAML) module is then designed to exploit the underlying relations between visible and infrared modalities for label noise modeling. Moreover, a Translation Supervision and Feature Augmentation (TSFA) module is designed to enhance the discriminability by enriching the supervision with feature augmentation and modality translation. The extensive experimental results demonstrate that our method significantly outperforms current state-of-the-art unsupervised methods under various settings, and even surpasses some supervised counterparts, providing a powerful baseline for UDA-VI-ReID.

Correlation filters (CFs) have been extensively used in tracking tasks due to their high efficiency although most of them regard the tracked target as a whole and are minimally effective in handling partial occlusion. In this study, we incorporate a part-based strategy into the framework of CFs and propose a novel multipart correlation tracker with triangle-structure constraints. Specifically, we train multiple CFs for the global object and local parts, which are then jointly applied to obtain the correlation response of any candidate during tracking. The tracker is robust in handling partial occlusion because of the use of part-based representation. The remaining global representation can contribute reliable cues in cases wherein several local filters drift away in a specific scene. We further propose a triangle-structure model to measure the structural similarity of candidates. The model employs multiple triangles to determine the spatial relationship among parts and helps constrain the location of the target. Moreover, we introduce an effective part selection scheme based on energy and integrity, which is generally applicable to part-tracking models. Extensive experiments on two public benchmarks demonstrate the superiority of the proposed method over the state-of-the-art approaches.

Prevalent federated learning commonly develops under the assumption that the ideal global class distributions are balanced. In contrast, real-world data typically follows the long-tailed class distribution, where models struggle to classify samples from tail classes. In this paper, we alleviate the issue under the long-tailed data, dissecting the into two aspects: the distorted feature space and the biased classifier. Specifically, we propose the Representation Unification and Classifier Rectification (RUCR), which leverages global unified prototypes to shape the feature space and calibrate the classifier. RUCR aggregates local prototypes (class-wise mean features) extracted by the global model to obtain global unified prototypes. It calibrates the feature space by pulling features within the same class towards corresponding global unified prototypes and pushing the other classes away. Moreover, RUCR utilizes global prototypes to reduce the classifier bias via prototypical mix-up. It generates a balanced virtual feature set by arbitrarily fusing global unified prototypes and local features. The classifier re-training is then conducted on the balanced virtual feature set to rectify the decision boundary and thus alleviate the shifts. Empirical results on CIFAR-10-LT, CIFAR-100-LT, and Tiny-Imagenet-LT datasets validate the superior performance of our proposed method.

Unsupervised visible-infrared person re-identification (US-VI-ReID) aims at learning a cross-modality matching model under unsupervised conditions, which is an extremely important task for practical nighttime surveillance to retrieve a specific identity. Previous advanced US-VI-ReID works mainly focus on associating the positive cross-modality identities to optimize the feature extractor by off-line manners, inevitably resulting in error accumulation of incorrect off-line cross-modality associations in each training epoch due to the intra-modality and inter-modality discrepancies. They ignore the direct cross-modality feature interaction in the training process, i.e ., the on-line representation learning and updating. Worse still, existing interaction methods are also susceptible to inter-modality differences, leading to unreliable heterogeneous neighborhood learning. To address the above issues, we propose a dual consistency-constrained learning framework (DCCL) simultaneously incorporating off-line cross-modality label refinement and on-line feature interaction learning. The basic idea is that the relations between cross-modality instance-instance and instance-identity should be consistent. More specifically, DCCL constructs an instance memory, an identity memory, and a domain memory for each modality. At the beginning of each training epoch, DCCL explores the off-line consistency of cross-modality instance-instance and instance-identity similarities to refine the reliable cross-modality identities. During the training, DCCL finds credible homogeneous and heterogeneous neighborhoods with on-line consistency between query-instance similarity and query-instance domain probability similarities for feature interaction in one batch, enhancing the robustness against intra-modality and inter-modality variations. Extensive experiments validate that our method significantly outperforms existing works, and even surpasses some supervised counterparts. The source code will be released.