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Register a new userRethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting
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Jian Jia
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Pedestrian attribute recognition aims to assign multiple attributes to one pedestrian image captured by a video surveillance camera. Although numerous methods are proposed and make tremendous progress, we argue that it is time to step back and analyze the status quo of the area. We review and rethink the recent progress from three perspectives. First, given that there is no explicit and complete definition of pedestrian attribute recognition, we formally define and distinguish pedestrian attribute recognition from other similar tasks. Second, based on the proposed definition, we expose the limitations of the existing datasets, which violate the academic norm and are inconsistent with the essential requirement of practical industry application. Thus, we propose two datasets, PETAtextsubscript{$ZS$} and RAPtextsubscript{$ZS$}, constructed following the zero-shot settings on pedestrian identity. In addition, we also introduce several realistic criteria for future pedestrian attribute dataset construction. Finally, we reimplement existing state-of-the-art methods and introduce a strong baseline method to give reliable evaluations and fair comparisons. Experiments are conducted on four existing datasets and two proposed datasets to measure progress on pedestrian attribute recognition.
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Despite various methods are proposed to make progress in pedestrian attribute recognition, a crucial problem on existing datasets is often neglected, namely, a large number of identical pedestrian identities in train and test set, which is not consistent with practical application. Thus, images of the same pedestrian identity in train set and test set are extremely similar, leading to overestimated performance of state-of-the-art methods on existing datasets. To address this problem, we propose two realistic datasets PETAtextsubscript{$zs$} and RAPv2textsubscript{$zs$} following zero-shot setting of pedestrian identities based on PETA and RAPv2 datasets. Furthermore, compared to our strong baseline method, we have observed that recent state-of-the-art methods can not make performance improvement on PETA, RAPv2, PETAtextsubscript{$zs$} and RAPv2textsubscript{$zs$}. Thus, through solving the inherent attribute imbalance in pedestrian attribute recognition, an efficient method is proposed to further improve the performance. Experiments on existing and proposed datasets verify the superiority of our method by achieving state-of-the-art performance.
In this paper, we aim to improve the dataset foundation for pedestrian attribute recognition in real surveillance scenarios. Recognition of human attributes, such as gender, and clothes types, has great prospects in real applications. However, the development of suitable benchmark datasets for attribute recognition remains lagged behind. Existing human attribute datasets are collected from various sources or an integration of pedestrian re-identification datasets. Such heterogeneous collection poses a big challenge on developing high quality fine-grained attribute recognition algorithms. Furthermore, human attribute recognition are generally severely affected by environmental or contextual factors, such as viewpoints, occlusions and body parts, while existing attribute datasets barely care about them. To tackle these problems, we build a Richly Annotated Pedestrian (RAP) dataset from real multi-camera surveillance scenarios with long term collection, where data samples are annotated with not only fine-grained human attributes but also environmental and contextual factors. RAP has in total 41,585 pedestrian samples, each of which is annotated with 72 attributes as well as viewpoints, occlusions, body parts information. To our knowledge, the RAP dataset is the largest pedestrian attribute dataset, which is expected to greatly promote the study of large-scale attribute recognition systems. Furthermore, we empirically analyze the effects of different environmental and contextual factors on pedestrian attribute recognition. Experimental results demonstrate that viewpoints, occlusions and body parts information could assist attribute recognition a lot in real applications.
In this paper, we first tackle the problem of pedestrian attribute recognition by video-based approach. The challenge mainly lies in spatial and temporal modeling and how to integrating them for effective and dynamic pedestrian representation. To solve this problem, a novel multi-task model based on the conventional neural network and temporal attention strategy is proposed. Since publicly available dataset is rare, two new large-scale video datasets with expanded attribute definition are presented, on which the effectiveness of both video-based pedestrian attribute recognition methods and the proposed new network architecture is well demonstrated. The two datasets are published on http://irip.buaa.edu.cn/mars_duke_attributes/index.html.
While recent studies on pedestrian attribute recognition have shown remarkable progress in leveraging complicated networks and attention mechanisms, most of them neglect the inter-image relations and an important prior: spatial consistency and semantic consistency of attributes under surveillance scenarios. The spatial locations of the same attribute should be consistent between different pedestrian images, eg, the ``hat attribute and the ``boots attribute are always located at the top and bottom of the picture respectively. In addition, the inherent semantic feature of the ``hat attribute should be consistent, whether it is a baseball cap, beret, or helmet. To fully exploit inter-image relations and aggregate human prior in the model learning process, we construct a Spatial and Semantic Consistency (SSC) framework that consists of two complementary regularizations to achieve spatial and semantic consistency for each attribute. Specifically, we first propose a spatial consistency regularization to focus on reliable and stable attribute-related regions. Based on the precise attribute locations, we further propose a semantic consistency regularization to extract intrinsic and discriminative semantic features. We conduct extensive experiments on popular benchmarks including PA100K, RAP, and PETA. Results show that the proposed method performs favorably against state-of-the-art methods without increasing parameters.
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Weichen Chen Collage ofn Information Engineering
, Zhejiang University of Technology
, Hangzhou
2021
Pedestrian attribute recognition in surveillance scenarios is still a challenging task due to inaccurate localization of specific attributes. In this paper, we propose a novel view-attribute localization method based on attention (VALA), which relies on the strong relevance between attributes and views to capture specific view-attributes and to localize attribute-corresponding areas by attention mechanism. A specific view-attribute is composed by the extracted attribute feature and four view scores which are predicted by view predictor as the confidences for attribute from different views. View-attribute is then delivered back to shallow network layers for supervising deep feature extraction. To explore the location of a view-attribute, regional attention is introduced to aggregate spatial information of the input attribute feature in height and width direction for constraining the image into a narrow range. Moreover, the inter-channel dependency of view-feature is embedded in the above two spatial directions. An attention attribute-specific region is gained after fining the narrow range by balancing the ratio of channel dependencies between height and width branches. The final view-attribute recognition outcome is obtained by combining the output of regional attention with the view scores from view predictor. Experiments on three wide datasets (RAP, RAPv2, PETA, and PA-100K) demonstrate the effectiveness of our approach compared with state-of-the-art methods.
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