Occluded person re-identification (Re-ID) focuses on addressing the occlusion problem when retrieving the person of interest across non-overlapping cameras. With the increasing demand for intelligent video surveillance and the application of person Re-ID technology, the real-world occlusion problem draws considerable interest from researchers. Although a large number of occluded person Re-ID methods have been proposed, there are few surveys that focus on occlusion. To fill this gap and help boost future research, this paper provides a systematic survey of occluded person Re-ID. In this paper, we review recent deep learning-based occluded person Re-ID researches. Firstly, we summarize the main issues caused by occlusion as four groups, i.e., position misalignment, scale misalignment, noisy information, and missing information. Secondly, we categorize existing methods into six solution groups, i.e., matching, image transformation, multi-scale features, attention mechanism, auxiliary information, and contextual recovery. We also discuss the characteristics of each approach, as well as the issues they addressed. Furthermore, we present the performance comparison of recent occluded person Re-ID methods on four public datasets: Partial-ReID, Partial-iLIDS, Occluded-ReID, and Occluded-DukeMTMC. We conclude the study with thoughts into promising future research directions.

Gait recognition is a promising biometric method that aims to identify pedestrians from their unique walking patterns. Silhouette modality, renowned for its easy acquisition, simple structure, sparse representation, and convenient modeling, has been widely employed in controlled in-the-lab research. However, as gait recognition rapidly advances from in-the-lab to in-the-wild scenarios, various conditions raise significant challenges for silhouette modality, including 1) unidentifiable low-quality silhouettes (abnormal segmentation, severe occlusion, or even non-human shape), and 2) identifiable but challenging silhouettes (background noise, non-standard posture, slight occlusion). To address these challenges, we revisit gait recognition pipeline and approach gait recognition from a quality perspective, namely QAGait. Specifically, we propose a series of cost-effective quality assessment strategies, including Maxmial Connect Area and Template Match to eliminate background noises and unidentifiable silhouettes, Alignment strategy to handle non-standard postures. We also propose two quality-aware loss functions to integrate silhouette quality into optimization within the embedding space. Extensive experiments demonstrate our QAGait can guarantee both gait reliability and performance enhancement. Furthermore, our quality assessment strategies can seamlessly integrate with existing gait datasets, showcasing our superiority. Code is available at https://github.com/wzb-bupt/QAGait.

Feedback is a fundamental mechanism existing in the human visual system, but has not been explored deeply in designing computer vision algorithms. In this paper, we claim that feedback plays a critical role in understanding convolutional neural networks (CNNs), e.g., how a neuron in CNNs describes an object's pattern, and how a collection of neurons form comprehensive perception to an object. To model the feedback in CNNs, we propose a novel model named Feedback CNN and develop two new processing algorithms, i.e., neural pathway pruning and pattern recovering. We mathematically prove that the proposed method can reach local optimum. Note that Feedback CNN belongs to weakly supervised methods and can be trained only using category-level labels. But it possesses a powerful capability to accurately localize and segment category-specific objects. We conduct extensive visualization analysis, and the results reveal the close relationship between neurons and object parts in Feedback CNN. Finally, we evaluate the proposed Feedback CNN over the tasks of weakly supervised object localization and segmentation, and the experimental results on ImageNet and Pascal VOC show that our method remarkably outperforms the state-of-the-art ones.

Gait recognition is a soft biotechnology to identify pedestrians observed from different camera views based on specific walking patterns. However, various dressing and wearing conditions bring great challenges to realistic gait recognition. Most existing methods take holistic gait silhouette as input and focus on local areas through horizontal strip division or attention map. We consider that this processing may contain mixed or incomplete information about multiple body parts so that gait information is misused or underutilized. In this paper, we propose a parsing-guided framework for gait recognition, named GaitParsing , which explores human semantic parsing to dissect human body into a set of specific and complete body parts. Correspondingly, a simple yet effective dual-branch feature extraction network is adopted to process holistic gait and distinct body parts. To maximize the use of highly discriminated gait frames, we propose a self-occlusion frame assessment to measure the self-occlusion in a gait sequence. Since there is no human parsing modality in current gait datasets, we further develop a general human parsing pipeline specifically tailored for gait datasets. This single training enables widespread application across various gait datasets. Extensive experiments with ablation analyses demonstrate competitive performance even in the most challenging conditions, e.g., Cloth-Changing (CC+5.9%). Especially, It is gratifying to see that our model can be easily applied to existing methods and significantly outperform the original architecture, even without much modification. Code is available at https://github.com/wzb-bupt/GaitParsing