Camera Style Research Articles

Logical Relation Inference and Multiview Information Interaction for Domain Adaptation Person Re-Identification.

Domain adaptation person re-identification (Re-ID) is a challenging task, which aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Recently, some clustering-based domain adaptation Re-ID methods have achieved great success. However, these methods ignore the inferior influence on pseudo-label prediction due to the different camera styles. The reliability of the pseudo-label plays a key role in domain adaptation Re-ID, while the different camera styles bring great challenges for pseudo-label prediction. To this end, a novel method is proposed, which bridges the gap of different cameras and extracts more discriminative features from an image. Specifically, an intra-to-intermechanism is introduced, in which samples from their own cameras are first grouped and then aligned at the class level across different cameras followed by our logical relation inference (LRI). Thanks to these strategies, the logical relationship between simple classes and hard classes is justified, preventing sample loss caused by discarding the hard samples. Furthermore, we also present a multiview information interaction (MvII) module that takes features of different images from the same pedestrian as patch tokens, obtaining the global consistency of a pedestrian that contributes to the discriminative feature extraction. Unlike the existing clustering-based methods, our method employs a two-stage framework that generates reliable pseudo-labels from the views of the intracamera and intercamera, respectively, to differentiate the camera styles, subsequently increasing its robustness. Extensive experiments on several benchmark datasets show that the proposed method outperforms a wide range of state-of-the-art methods. The source code has been released at https://github.com/lhf12278/LRIMV.

Real-world video superresolution enhancement method based on the adaptive down-sampling model

With the 5G and the popularity of high-definition and ultrahigh-definition equipment, people have increasingly higher requirements for the resolution of images or videos. However, the transmission pressure on servers is also gradually increasing. Therefore, superresolution technology has attracted much attention in recent years. Simultaneously, with the further development of deep learning techniques, superresolution research is shifting from the calculation of traditional algorithms to the deep learning method, which exhibits a greatly superior final display. First, the traditional block-matching-3D (BM3D) algorithm is formed as the postprocessing module, which can avoid the uneven edge of GAN network recovery, make the picture appear more authentic, and improve the viewer’s subjective feelings. Next, the adaptive-downsampling model (ADM) is utilized to train models for specific camera styles. The high-resolution (HR) data sequence is subsequently downsampled to a low-resolution (LR) data sequence, enabling the superresolution algorithm to utilize this training set. This method can obtain better results and improve overall performance by 0.1~0.3 dB.

Intra-Inter Domain Similarity for Unsupervised Person Re-Identification.

Most of unsupervised person Re-Identification (ReID) works produce pseudo-labels by measuring the feature similarity without considering the domain discrepancy among cameras, leading to degraded accuracy in pseudo-label computation across cameras. This paper targets to address this challenge by decomposing the similarity computation into two stages, i.e., the intra-domain and inter-domain computations, respectively. The intra-domain similarity directly leverages CNN features learned within each camera, hence generates pseudo-labels on different cameras to train the ReID model in a multi-branch network. The inter-domain similarity considers the classification scores of each sample on different cameras as a new feature vector. This new feature effectively alleviates the domain discrepancy among cameras and generates more reliable pseudo-labels. We further propose the Instance and Camera Style Normalization (ICSN) to enhance the robustness to domain discrepancy. ICSN alleviates the intra-camera variations by adaptively learning a combination of instance and batch normalization. ICSN also boosts the robustness to inter-camera variations through TNorm which converts the original style of features into target styles. The proposed method achieves competitive performance on multiple datasets under fully unsupervised, intra-camera supervised and domain generalization settings, e.g., it achieves rank-1 accuracy of 64.4% on the MSMT17 dataset, outperforming the recent unsupervised methods by 20+%.

Asymmetric double networks mutual teaching for unsupervised person Re-identification

Unsupervised person re-identification (Re-ID) has always been challenging in computer vision. It has received much attention from researchers because it does not require any labeled information and can be freely deployed to new scenarios. Most unsupervised person Re-ID research studies produce and optimize pseudo-labels by iterative clustering algorithms on a single network. However, these methods are easily affected by noisy labels and feature variations caused by camera shifts, which will limit the optimization of pseudo-labels. In this paper, we propose an Asymmetric Double Networks Mutual Teaching (ADNMT) architecture that uses two asymmetric networks to generate pseudo-labels for each other by clustering, and the pseudo-labels are updated and optimized by alternate training. Specifically, ADNMT contains two asymmetric networks. One network is a multiple granularity network, which extracts pedestrian features of multiple granularity that correspond to numerous classifiers, and the other network is a conventional backbone network, which extracts pedestrian features that correspond to a classifier. Furthermore, because the camera style changes seriously affect the generalization ability of the proposed model, this paper designs Similarity Compensation of Inter-Camera (SCIC) and Similarity Suppression of Intra-Camera (SSIC) according to the camera ID of the pedestrian images to optimize the similarity measure. Extensive experiments on multiple Re-ID benchmark datasets show that our proposed method achieves superior performance compared with the state‐of‐the‐art unsupervised person re-identification methods.

An Improved CycleGAN for Data Augmentation in Person Re-Identification

Person re-identification (ReID) has attracted more and more attention, which is to retrieve interested persons across multiple non-overlapping cameras. Matching the same person between different camera styles has always been an enormous challenge. In the existing work, cross-camera styles images generated by the cycle-consistent generative adversarial network (CycleGAN) only transfer the camera resolution and ambient lighting. The generated images produce considerable redundancy and inappropriate pictures at the same time. Although the data is added to prevent over-fitting, it also makes significant noise, so the accuracy is not significantly improved. In this paper, an improved CycleGAN is proposed to generate images for achieving improved data augmentation. The transfer of pedestrian posture is added at the same time as transferring the image style. It not only increases the diversity of pedestrian posture but also reduces the domain gap caused by the style change between cameras. Besides, through the multi-pseudo regularized label (MpRL), the generated images are assigned virtual tags dynamically in training. Through many experimental evaluations, we have achieved a very high identification accuracy on Market-1501, DukeMTMC-reID, and CUHK03-NP datasets. On the three datasets, the quantitative results of mAP are 96.20%, 93.72%, and 86.65%, and the quantitative results of rank-1 are 98.27%, 95.37%, and 90.71%, respectively. The experimental results fully show the superiority of our proposed method.

The Influence of Camera Style on the Film in a Documentary

The Influence of Camera Style on the Film in a Documentary

Learning Invariance from Generated Variance for Unsupervised Person Re-identification.

This work focuses on unsupervised representation learning in person re-identification (ReID). Recent self-supervised contrastive learning methods learn invariance by maximizing the representation similarity between two augmented views of a same image. However, traditional data augmentation may bring to the fore undesirable distortions on identity features, which is not always favorable in id-sensitive ReID tasks. In this paper, we propose to replace traditional data augmentation with a generative adversarial network (GAN) that is targeted to generate augmented views for contrastive learning. A 3D mesh guided person image generator is proposed to disentangle a person image into id-related and id-unrelated features. Deviating from previous GAN-based ReID methods that only work in id-unrelated space (pose and camera style), we conduct GAN-based augmentation on both id-unrelated and id-related features. We further propose specific contrastive losses to help our network learn invariance from id-unrelated and id-related augmentations. By jointly training the generative and the contrastive modules, our method achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.

Camera-irrelevant Feature Invariance Learning for Unsupervised Person Re-identification

Unsupervised person re-identification (re-ID) aims to match pictures of target pedestrians from some other cameras without any labels. The existing methods usually adopt a cluster-based pipeline to generate false labels for training. However, because of the differences in camera configuration, taking pictures Angle, illumination intensity and pedestrian background, unlabeled pedestrian training samples captured by different cameras are hard to classify into a cluster. To solve this problem, the paper proposes a camera-irrelevant feature learning framework, which learns camera-irrelevant features and camera-irrelevant distances through network training and clustering. Specifically, during training, images of other camera styles are generated, which enriches cross-camera learning. During clustering, we propose a camera-irrelevant distance matrix, which reduces the inter-class variance caused by camera variance.

Multiple camera styles learning for unsupervised person re-identification

Most clustering-based unsupervised person re-identification methods usually first assign pseudo-labels to pedestrian images using a clustering algorithm and then train a network using the labeled pedestrian images in each iteration. However, clustering algorithms may produce some clusters that only contain pedestrian images from fewer cameras, thus resulting in these clusters having fewer camera styles. Training a network using the pedestrian images from these clusters may degrade the capability of the network to retrieve pedestrians across cameras. Moreover, unsupervised learning methods usually utilize only contrastive loss that is based on self-supervised learning. However, lack of metric loss with mandatory guidance from labels may lead the network to fail to learn sufficiently the discriminative information of pedestrians. To address these issues, we propose a multiple camera styles learning (MCSL) method for unsupervised person re-identification. Specifically, we design a novel cluster filtration module (CFM) to ensure the clustering algorithm to keep the focus on pedestrian identities rather than camera styles by filtering out the clusters with fewer camera styles. In addition, we also design a multi-granularity feature metric (MGFM) loss to introduce metric learning with labels into unsupervised learning. The effectiveness of the proposed MCSL is demonstrated by the experimental results (Rank-1 accuracy) on Market1501 (93.7%), DukeMTMC(86.0%) and MSMT17 (71.12%), significantly reducing the gap between unsupervised and supervised performance on person re-identification.

Unsupervised Domain Adaptation for Person Re-Identification Via Individual-Preserving and Environmental-Switching Cyclic Generation

Unsupervised domain adaptation for person re-identification (Re-ID) suffers severe domain discrepancies between source and target domains. To reduce the domain shift caused by the changes of context, camera style, or viewpoint, existing methods in this field fine-tune and adapt the Re-ID model with augmented samples, either translating source samples to the target style or assigning pseudo labels to the target. The former methods may lose identity details but keep redundant source background during translation. In contrast, the latter techniques may give noisy labels when the model meets the unseen background and person pose. We mitigate the domain shift in the former translation direction by cyclically decoupling environment and identity-related features. We propose a novel individual-preserving and environmental-switching cyclic generation network (IPES-GAN). Our network has the following distinct features: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Decoupled features instead of fused features:</i> we encode the images into an individual part and an environmental part, which are proved beneficial to generation and adaptation; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cyclic generation instead of one-step adaptive generation</i> . We swap source and target environment features to generate cross-domain images with preserved identity-related features conditioned with source (target) background features and then changed again to generate back the input image so that cyclic generation runs in a self-supervised way. Experiments carried out on two significant benchmarks: Market-1501 and DukeMTMC-Reid, reveal state-of-the-art performance.

Feature-Level Camera Style Transfer for Person Re-Identification

The person re-identification (re-ID) problem has attracted growing interest in the computer vision community. Most public re-ID datasets are captured by multiple non-overlapping cameras, and the same person may appear dissimilar in different camera views due to variances of illuminations, viewpoints and postures. These differences, collectively referred to as camera style variance, make person re-ID still a challenging problem. Recently, researchers have attempted to solve this problem using generative models. The generative adversarial network (GAN) is widely used for the pose transfer or data augmentation to bridge the camera style gap. However, these methods, mostly based on image-level GAN, require huge computational power during the training of generative models. Furthermore, the training process of GAN is separated from the re-ID model, which makes it hard to achieve a global optimal for both models simultaneously. In this paper, the authors propose to alleviate camera style variance in the re-ID problem by adopting a feature-level Camera Style Transfer (CST) model, which can serve as an intra-class augmentation method and enhance the model robustness against camera style variance. Specifically, the proposed CST method transfers the camera style-related information of input features while preserving the corresponding identity information. Moreover, the training process can be embedded into the re-ID model in an end-to-end manner, which means the proposed approach can be deployed with much less time and memory cost. The proposed approach is verified on several different person re-ID baselines. Extensive experiments show the validity of the proposed CST model and its benefits for re-ID performance on the Market-1501 dataset.

Dual-stream Reciprocal Disentanglement Learning for domain adaptation person re-identification

Since human-labeled samples are free for the target set, unsupervised person re-identification (Re-ID) has attracted much attention in recent years, by additionally exploiting the source set. However, due to the differences on camera styles, illumination and backgrounds, there exists a large gap between source domain and target domain, introducing a great challenge on cross-domain matching. To tackle this problem, in this paper we propose a novel method named Dual-stream Reciprocal Disentanglement Learning (DRDL), which is quite efficient in learning domain-invariant features. In DRDL, two encoders are first constructed for id-related and id-unrelated feature extractions, which are respectively measured by their associated classifiers. Furthermore, followed by an adversarial learning strategy, both streams reciprocally and positively effect each other, so that the id-related features and id-unrelated features are completely disentangled from a given image, allowing the encoder to be powerful enough to obtain the discriminative but domain-invariant features. In contrast to existing approaches, our proposed method is free from image generation, which not only reduces the computational complexity remarkably, but also removes redundant information from id-related features. Extensive experiments substantiate the superiority of our proposed method compared with the state-of-the-arts. The source code has been released in https://github.com/lhf12278/DRDL.

Bi-Directional Style Adaptation Network for Person Re-Identification

Large-scale surveillance camera system can provide multiple visual information. Nevertheless, the images exhibit diverse appearance features due to different parameters and installation positions of the cameras. This camera style variation deteriorates its benefit from capturing identity features in person re-identification (Re-Id). The existing methods for filtering shallow appearance information through the Instance Normalization (IN) layer are extremely unfavorable to supervised tasks. To mitigate this problem, a simplified and valid Bidirectional Style Adaptation Network (BSA-Net) is presented to incorporate a new branch containing IN layer to learn invariant features with changing appearance. For these two branches, the structure is completely independent and the parameters are partially shared. BAS-Net is able to focus on the extraction of identity information while preserving appearance features. Specially, this new branch is removed during the testing phase, which significantly facilitates performance without introducing new computation. The superiority of the model is confirmed in extensive experiments on widely used benchmarks.

Detroit dir. by Kathryn Bigelow

Reviewed by: Detroit dir. by Kathryn Bigelow Jacob DeBrock Detroit (dir. Kathryn Bigelow, 2017) The Midwest is commonly associated with images of solitary farmhouses, water towers, grain silos, and football fields that dominate the high schools next to them. In this view, the Midwest is rural and predominantly White. However, these images ignore the reality of the Midwest's diverse populations (in both urban and rural spaces), many of whom have seen little onscreen representation and much oppression throughout this country's history. Director Kathryn Bigelow's historical drama Detroit (2017) gives viewers a chance to reimagine midwestern experiences of the civil rights era via a recreation of the 1967 Detroit Rebellion. The film depicts events at the Algiers Motel, where police officers killed and tortured (physically and psychologically) both Black and White civilians. Despite the film's flaws, its resolve to showcase a more diverse Midwest is admirable. This review is concerned with three main elements of Detroit: its attention to historical context; its emphasis on the urban Midwest; and the specific form of race relations that it showcases during and after the rebellion. Bigelow highlights many aspects of 1960s culture and history in the film, from the supremacy of Motown artists to the impact of the Vietnam War on the Black community. An early scene depicts a homecoming party for a group of veterans; Karl Greene (Anthony Mackie), one of the Black civilians later caught at the Algiers, is also a veteran. Throughout its runtime, Detroit more narrowly focuses on changes happening within northern and midwestern cities—especially in Black communities—over the years leading up to 1967. The opening scenes illustrate the general unrest that created aggressive attitudes between working-class Black residents and the police, who often harassed Black people and businesses and committed acts of brutality. For instance, the primary police character, Philip Krauss (Will Poulter), is introduced onscreen by shooting a Black man, Leon (Tyler James Williams), in the back. The man had been looting a store and eventually dies from the injury. Despite such scenes, the film handicaps its [End Page 97] ability to fully represent this historical context by quickly moving from a broader view of the rebellion to the specific characters at the Algiers Motel. Sticking to a hyperlink style of storytelling for the entire narrative would have given Detroit a greater ability to depict how the rebellion impacted huge swaths of Detroit's population and how this event helped shift the media's images of the civil rights era from southern protesters to northern and midwestern disturbances. Because Detroit heavily focuses on the days of the rebellion and its aftermath, viewers are not given an opportunity to consider what the city's Black community was like before these events. What Black-owned businesses were most common in Detroit and how many of them were lost or damaged in the rebellion? How did the rebellion tie into concerns over industrial jobs or the lack thereof? There is a mention of the Ford Motor Company closing its factory, but little context beyond that line. How did the rebellion affect the long-term perceptions of Detroit and the urban Midwest, both within the city and among outsiders? Instead of such details, Bigelow gives viewers the same images that were available over fifty years ago: people looting stores, the burnt-out shells of buildings, police and National Guard troops making Detroit look like a war zone, and so on. Notably, Bigelow uses a faux-documentary camera style of movement, which adds a jittery edge to the proceedings that both matches and contrasts with the historical newsreel footage that is edited throughout the film. Still, there is much the filmmakers could have done beyond regurgitating commonly seen images of urban unrest in the civil rights era. Creating new or unique views of the moment would have provided a more complex perspective on these events. The movie's portrayal of the Black characters in Detroit and their connections with the Midwest is mixed, including both positive and negative depictions. On the positive side, most of the main and secondary Black characters are presented in a sympathetic fashion and not as foreigners or outsiders in their own...

Close-set camera style distribution alignment for single camera person re-identification

The purpose of person re-identification (ReID) is to find the same person under different cameras and the basic difficulty lies in the need for large amounts of cross-camera pedestrian annotations. In reality, annotating cross-camera pedestrians is time-consuming especially in large-scale surveillance camera networks. This paper focuses on addressing the ReID problem under single-camera training (SCT) setting, where each person of the training set only appears in one camera. Due to the lack of cross-camera pedestrian annotations, it is difficult to effectively eliminate the camera style interference by narrowing the distance between the image features of the same person. To address this problem, we propose a close-set camera style distribution alignment (C2SDA) framework for SCT ReID. In order to reduce the camera-style interference from both instance- and distribution-levels simultaneously, we first design an instance-distribution camera style alignment module that directly aligns the feature distribution of input images under each camera and then trains the model at instance level with the aligned features. Secondly, we further design an augment close-set camera style distribution module that transforms the camera feature distribution alignment problem from open-set into close-set one, which preserving the discriminative ability of features during the alignment process. Experimental results verify that our framework can significantly improve the ReID performance under SCT setting and surpass the current SOTA methods. The source code is available athttps://github.com/HongweiZhang97/CCSDA.

Person Re-Identification Combined with Style Transfer and Pose Generation

The number of existing person re-identification datasets is limited, and there are a series of changes such as illumination, background occlusion and pose among each dataset, which makes it difficult for the existing methods to learn robust feature representation, leading to a decline in recognition performance. To solve these problems, a person re-identification method combining style and pose generation is proposed in this paper. First with the impact of camera style differences in collecting images from different cameras, a style transformation method based on generative adversarial network is introduced into a person re-identification model, and cyclic generative adversarial networks (CycleGAN) is used to realize style transfer and reduce the influence of camera differences. Second, in view of the problem that when pedestrian pose changes greatly, easy to ignore identity-sensitive related information, AlphaPose is introduced to implement pose estimation. Combining style and posture for the first time and using improved deep convolution generative adversarial network (DCGAN) structure enrich the input sample information and generate unified style pose image; while using new synthetic data to train person re-identification network model improves the recognition performance of the model. Finally, further introducing random erasure method during data enhancement, in order to reduce the overfitting phenomena, improves the generalization ability of the network simultaneously and solves partial occlusion. The experimental results show that the proposed method outperforms typical style-based or pose-based methods. The accuracy of rank-1 and mAP on Market-1501 dataset is 90.4% and 74.5%, respectively, which are 2.28% and 5.78% higher, respectively. To a certain extent, the performance of person re-identification is improved.

Using Cameras for Precise Measurement of Two-Dimensional Plant Features: CASS.

Images are used frequently in plant phenotyping to capture measurements. This chapter offers a repeatable method for capturing two-dimensional measurements of plant parts in field or laboratory settings using a variety of camera styles (cellular phone, DSLR), with the addition of a printed calibration pattern. The method is based on calibrating the camera using information available from the EXIF tags from the image, as well as visual information from the pattern. Code is provided to implement the method, as well as a dataset for testing. We include steps to verify protocol correctness by imaging an artifact. The use of this protocol for two-dimensional plant phenotyping will allow data capture from different cameras and environments, with comparison on the same physical scale. We abbreviate this method as CASS, CAmera aS Scanner.

A Domain Adaptive Person Re-Identification Based on Dual Attention Mechanism and Camstyle Transfer

Due to the variation in the image capturing process, the difference between source and target sets causes a challenge in unsupervised domain adaptation (UDA) on person re-identification (re-ID). Given a labeled source training set and an unlabeled target training set, this paper focuses on improving the generalization ability of the re-ID model on the target testing set. The proposed method enforces two properties at the same time: (1) camera invariance is achieved through the positive learning formed by unlabeled target images and their camera style transfer counterparts; and (2) the robustness of the backbone network feature extraction is improved, and the accuracy of feature extraction is enhanced by adding a position-channel dual attention mechanism. The proposed network model uses a classic dual-stream network. Comparative experimental results on three public benchmarks prove the superiority of the proposed method.

Camera keyframing with style and control

We present a novel technique that enables 3D artists to synthesize camera motions in virtual environments following a camera style , while enforcing user-designed camera keyframes as constraints along the sequence. To solve this constrained motion in-betweening problem, we design and train a camera motion generator from a collection of temporal cinematic features (camera and actor motions) using a conditioning on target keyframes. We further condition the generator with a style code to control how to perform the interpolation between the keyframes. Style codes are generated by training a second network that encodes different camera behaviors in a compact latent space, the camera style space. Camera behaviors are defined as temporal correlations between actor features and camera motions and can be extracted from real or synthetic film clips. We further extend the system by incorporating a fine control of camera speed and direction via a hidden state mapping technique. We evaluate our method on two aspects: i) the capacity to synthesize style-aware camera trajectories with user defined keyframes; and ii) the capacity to ensure that in-between motions still comply with the reference camera style while satisfying the keyframe constraints. As a result, our system is the first style-aware keyframe in-betweening technique for camera control that balances style-driven automation with precise and interactive control of keyframes.

Camera style transformation with preserved self-similarity and domain-dissimilarity in unsupervised person re-identification

The inconsistency caused by different factors, such as different camera imaging methods, complex imaging environments, and changes in light, present a huge challenge to person re-identification (re-ID). Unsupervised domain adaptation (UDA) can solve the inconsistency issue to a certain extent, but different datasets may not have any overlapping of people’s identities. Therefore, it is necessary to pay attention to people’s identities in solving domain-dissimilarity. A camera imaging style transformation with preserved self-similarity and domain-dissimilarity (CSPSD) is proposed to solve the cross-domain issue in person re-ID. First, CycleGAN is applied to determine the style conversion between source and target domains. Intra-domain identity constraints are used to maintain identity consistency between source and target domains during the image style transformation process. Maximum mean difference (MMD) is used to reduce the difference in feature distribution between source and target domains. Then, a one-to-n mapping method is proposed to achieve the mapping between positive pairs and distinguish negative pairs. Any sample image from the source domain and its transformed image or a transformed image with the same identity information compose a positive pair. The transformed image and any image from the target domain compose a negative pair. Next, a circle loss function is used to improve the learning speed of positive and negative pairs. Finally, the proposed CSPSD that can effectively reduce the difference between domains and an existing feature learning network work together to learn a person re-ID model. The proposed method is applied to three public datasets, Market-1501, DukeMTMC-reID, and MSMT17. The comparative experimental results confirm the proposed method can achieve highly competitive recognition accuracy in person re-ID.