The challenge the team set out to solve is familiar to anyone who has watched footage of a busy train station or street crossing. In dense crowds, pedestrians constantly overlap and occlude one another, so the visible portion of any given person may shrink to a sliver of a head or shoulder. As visual information passes through the successive layers of a convolutional neural network, this already sparse evidence degrades further, a phenomenon the researchers describe as severe feature information loss. Standard detection architectures, which rely on pyramids of features at different resolutions, tend to blend this degraded information indiscriminately, drowning weak signals from occluded people in redundant background data.

The new model, developed by Rui Ke, Shuzhen Wan and Guoqing Xiao and published in the International Journal of Machine Learning and Cybernetics, attacks the problem in the frequency domain. At its core is the Haar wavelet transform, a classic mathematical operation that splits an image or feature map into low-frequency components, which capture smooth, slowly varying structure, and high-frequency components, which encode sharp edges and fine detail. Instead of the usual downsampling by strided convolution, which discards information as resolution drops, the wavelet decomposition module retains both frequency bands in a lossless manner. That means the fine-grained cues that might reveal an occluded pedestrian, such as the contour of a shoulder or the texture of clothing, are preserved rather than thrown away.

Building on this decomposition, the researchers designed a waveform frequency attention module that learns which frequency bands matter for the task at hand. Attention mechanisms have become a staple of modern deep learning, allowing networks to reweight features adaptively instead of treating all inputs equally. Here, the reweighting happens in the frequency domain: the network can amplify high-frequency responses when fine edges are informative and lean on low-frequency content when broader structure is more reliable. This is particularly useful in crowded scenes, where the discriminative evidence for a partially hidden person often lives in narrow, high-frequency edge fragments.

The team went further by constructing two novel attention mechanisms, a frequency channel attention mechanism and a frequency triplet attention mechanism. The former extends the influential squeeze-and-excitation style of channel attention, which recalibrates the importance of each feature channel, into the frequency domain, inspired by work such as FcaNet, which showed that frequency analysis can produce powerful channel attention without extra parameters. The latter adapts the triplet attention concept, which captures interactions across spatial and channel dimensions, to frequency-decomposed features. Together, these modules reweight features from different frequency bands so that cross-scale fusion combines complementary information rather than redundant noise.

Why does frequency-domain processing make such a difference for dense detection? The authors point to the problem of information redundancy in convolutional networks. Neighboring pixels and adjacent feature channels are highly correlated, so much of what a conventional network computes is repetitive. Frequency transforms decorrelate this information, concentrating the meaningful signal into compact representations. Related research has exploited this insight for image compression, super-resolution, semantic segmentation and even the detection of camouflaged objects, whose concealment often depends on subtle frequency signatures. The new work brings the same principle to the crowded-street problem, where it also confers an unexpected benefit: efficiency.

That efficiency gain is one of the most striking aspects of the study. On the WiderPerson dataset, the model improved mean Average Precision, the standard accuracy metric for detection, by approximately 2.1 percent over the baseline, and on CrowdHuman, a large benchmark for detecting humans in crowds, the gain was roughly 3.9 percent. Meanwhile, the number of parameters fell by 39.7 percent and floating-point operations, a measure of computational cost, dropped by 33.4 percent. In other words, the model is simultaneously more accurate and substantially lighter, a combination that is rare in a field where gains usually come at the price of larger networks. For deployment on edge devices such as surveillance cameras, drones or in-vehicle systems, that lighter footprint matters as much as the accuracy boost.

The implications extend well beyond counting people in crowds. Accurate pedestrian detection underpins autonomous driving systems, smart city surveillance, traffic management and robotics, all settings where occlusion is the norm rather than the exception. A detector that can reliably pick out people obscured by others, at low computational cost, could make real-time safety systems more dependable. The researchers have made their source code publicly available on GitHub, which should allow other teams to integrate the wavelet-based modules into their own detection pipelines and test them on additional scenarios, from nighttime streets to aerial imagery.

The study also fits into a broader movement in machine learning toward thinking beyond the spatial pixel grid. Wavelet-based approaches have recently been unified with transformers for visual representation, used in remote sensing super-resolution and applied to change detection in radar imagery, while other groups have shown that networks can learn directly in the frequency domain to cut inference time. What distinguishes the new model is the way it weaves frequency-domain decomposition through the entire feature fusion process, from the initial wavelet downsampling to the frequency channel and triplet attention mechanisms that guide cross-scale integration.

For a field that has been dominated by ever-larger architectures, the message of this research is refreshingly counterintuitive: sometimes the path to better perception is not more computation, but smarter mathematics. By treating visual features as signals with meaningful frequency structure, the model recovers information that brute-force networks discard, sees people that would otherwise vanish into the crowd, and does so with a fraction of the parameters. As cities grow denser and machines are asked to navigate them, that combination of accuracy, efficiency and mathematical elegance may prove hard to ignore.

Subject of Research: Wavelet-transform-based cross-scale feature fusion for dense pedestrian object detection in occluded scenes

Article Title: Wavelet-transform-based cross-scale dense pedestrian object detection model

Article References: Ke, R., Wan, S., & Xiao, G. (2026). Wavelet-transform-based cross-scale dense pedestrian object detection model. International Journal of Machine Learning and Cybernetics, 17(11), Article 497. https://doi.org/10.1007/s13042-026-03328-6

Image Credits: AI Generated

DOI: 10.1007/s13042-026-03328-6

Keywords: pedestrian detection, wavelet transform, computer vision, object detection, attention mechanism, deep learning, frequency domain, crowded scenes, feature fusion, Haar wavelet, lightweight models, CrowdHuman

The researchers assembled a baseline of 33 U.S. testing locations known to be in use in 2019, then added six additional cities documented as automated vehicle testing sites in 2025. Comparing the two snapshots allowed them to track how the geography of testing has evolved over a period when the industry matured dramatically. Using U.S. Census Bureau QuickFacts data and other public sources, they characterized each site by racial and ethnic composition, disability prevalence, poverty rates, age structure, gender balance, bicycling rates, and economic context. The goal was not simply to map where testing happens but to quantify what kinds of people and conditions the vehicles encounter during the millions of miles that feed their development.

Several trends emerged from the comparison. The share of residents identifying as White alone declined across many testing cities, a shift the authors note is consistent with changes in how the 2020 Census measured and processed race data, including a marked increase in multiracial reporting. Disability shares rose modestly in the tested locations, reflecting both demographic reality and improved measurement. Poverty rates declined modestly in many of the cities studied. None of these shifts, however, altered the study’s central finding: the sites where autonomous vehicles gain public-road experience remain overwhelmingly urban, even though roughly one in five Americans lives in a rural area and rural roads carry a disproportionate share of fatal crashes.

That urban bias matters for reasons that go beyond fairness optics. Rural driving presents conditions that city miles rarely supply: high-speed two-lane roads with narrow or absent shoulders, unmarked lanes, gravel surfaces, wildlife crossings, infrequent lighting, and long stretches with no cellular coverage. Emergency response times are longer, and the consequences of a perception failure can be more severe. The study’s reference literature documents these challenges in detail, including reviews of autonomous vehicle deployment in rural areas and federal initiatives such as the U.S. Department of Transportation’s Rural Autonomous Vehicle research program launched in April 2025. A vehicle trained and validated almost exclusively on dense urban grids may simply lack the exposure needed to handle a deer at dusk on a farm road.

The demographic dimension is equally consequential. Modern perception systems are built on machine learning, and machine learning inherits the biases of its training data. The study cites a growing body of fairness research showing that object detection systems can exhibit predictive inequity across demographic groups, and that pedestrian detection performance can degrade depending on pose, occlusion, and skin tone. Pedestrians who use wheelchairs face documented disparities in road crash mortality, yet they remain rare in typical training and testing exposure. If a vehicle’s sensors and decision algorithms are tuned primarily on the populations of a handful of tech-friendly cities, the safety case for deployment elsewhere rests on assumptions the data may not support.

What distinguishes this analysis is its insistence that diversity in testing must extend beyond geography and demographics into behavioral and data-pathway domains. Behavioral diversity means ensuring that vehicles encounter the full range of road user conduct: bicyclists commuting in large numbers, pedestrians with varied gait patterns and mobility aids, drivers who behave in ways that differ from the norms of San Francisco or Phoenix. Data-pathway diversity addresses a subtler problem: it is not enough for a vehicle to physically pass through a community. The system must actually record and learn from its interactions with under-represented groups, so that those encounters flow into the development pipeline rather than vanishing as unlogged miles.

The authors also introduce the idea of public-exposure diversity, arguing that trust in autonomous vehicles is built through visible, repeated, equitable contact with the technology. Communities that never see a self-driving car during its formative testing years may be asked to accept a system they had no role in shaping, a dynamic the study links to research on public perception and adoption. Perception studies cited in the paper show that people’s comfort with automated vehicles is influenced by demographic and contextual factors, and that automation itself can alter how pedestrians judge safety. Exposure, in other words, is not just a data collection strategy; it is a social contract that determines who benefits from and who trusts the technology.

To make the framework actionable, the researchers propose practical criteria for diversified pilot programs, spanning rural share of vehicle miles traveled, racial and ethnic representation, disability visibility, gender balance, bicycling prevalence, and economic context. These metrics give operators, regulators, and municipalities a common vocabulary for evaluating whether a testing program is genuinely broadening its exposure or simply accumulating more miles in familiar territory. The framework is explicitly complementary to simulator-based and dataset-driven testing, which can scale virtually but cannot guarantee that the underlying data reflects the full population. Where and how exposure occurs, the authors argue, provides a population-level foundation for safety, trust, and equity that no simulation alone can supply.

The study’s own data caveats are worth noting. The apparent decline in White alone shares across testing sites partly reflects Census measurement and processing changes rather than dramatic demographic turnover, and the modest trends in disability and poverty shares are sensitive to how those categories are defined and reported. The authors are transparent that their location list, drawn from public documentation including the Bloomberg Philanthropies AVs in Cities guide and Census QuickFacts, captures documented testing rather than every mile driven by every operator. Still, the pattern is robust enough to support the central claim: the industry’s real-world learning environment is narrower than the population it intends to serve.

The stakes of this gap are not hypothetical. The 2018 fatality involving a self-driving Uber test vehicle in Arizona remains a defining moment for the field, and subsequent research on crash injury trends has emphasized how occupant and road user characteristics shape outcomes. As autonomous vehicles move from pilots toward commercial deployment, the question of whose streets, whose bodies, and whose behaviors are represented in their training and validation becomes a matter of public health and social justice, not merely engineering. This analysis offers a measurable way to hold the industry accountable, arguing that the road to trustworthy autonomy must be paved with testing that looks like America in all its demographic, geographic, and behavioral variety, and that the miles a self-driving car logs should count only if the people it encounters are the people it will one day carry and protect.

Subject of Research: Diversity of autonomous vehicle testing sites in the United States

Article Title: An analysis of autonomous vehicle testing site diversity

Article References: Gettel, O., Gates, C. W., Gettel, C. D., Huggins, J., & Peters, D. (2026). An analysis of autonomous vehicle testing site diversity. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03269-2

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03269-2

Keywords: autonomous vehicles, testing sites, diversity, rural roads, algorithmic bias, pedestrian detection, public trust, equity, Census data, vehicle miles traveled, machine learning, transportation safety

The researchers call their system DECA-YOLO, a lightweight enhancement of the popular YOLOv7 object detection architecture. YOLO, which stands for You Only Look Once, has become the workhorse of real-time detection since its introduction in 2016 because it processes an entire image in a single pass through a convolutional neural network, rather than proposing candidate regions and classifying them one by one. That single-pass design is what makes YOLO fast enough for applications like vehicle perception, where a detector must produce results many times per second. But speed alone is not enough. The features that a network extracts from an image vary enormously in their usefulness, and standard YOLO architectures treat many of these feature channels with equal weight, which can dilute the signals that actually matter for distinguishing a person from a lamppost, a shadow, or the crowd behind them.

To sharpen that discrimination, the authors integrate a module they call Dual-Pooling Efficient Channel Attention, or DECA, into the backbone of YOLOv7. Channel attention is a well-established idea in computer vision: the network learns to assign an importance weight to each of its feature channels, amplifying the ones that carry useful information and suppressing the ones that do not. The most famous implementation, the Squeeze-and-Excitation Network published in 2018, compresses each channel down to a single number using global average pooling and then uses fully connected layers to compute the attention weights. The Efficient Channel Attention network, or ECA-Net, later streamlined this by replacing the fully connected layers with a one-dimensional convolution that lets neighboring channels interact directly, dramatically reducing the parameter count while preserving performance.

What DECA adds to this lineage is a second pooling operation. Traditional channel attention mechanisms typically rely solely on global average pooling, which summarizes each channel by averaging its activation values across the entire spatial extent of the feature map. Average pooling is excellent at preserving the overall contextual information in a channel, but it can wash out sharp, localized responses, the very signals that indicate the presence of a partially occluded pedestrian peeking out from behind a car or another person. DECA therefore combines global average pooling with global max pooling. Max pooling extracts the single strongest activation in each channel, capturing the most salient features regardless of how small the region they occupy. By feeding both summaries into the attention computation, the module gains access to two complementary views of what each channel contains: the broad context and the sharpest evidence.

After the dual pooling, DECA applies a one-dimensional convolution to enable efficient cross-channel interactions, following the design philosophy of ECA-Net. Crucially, the entire module avoids fully connected layers and does not include a spatial attention branch, the two components that make many competing attention mechanisms computationally expensive. This restraint is deliberate. The authors argue that existing detection models often employ complex attention mechanisms that improve accuracy on benchmarks but add enough computational overhead to undermine the real-time guarantees that pedestrian detection demands. In occluded and crowded environments, these heavy mechanisms may also struggle because of insufficient feature selectivity, attending to the wrong regions or channels when the scene is visually chaotic. DECA’s design enhances discriminative feature representation while keeping the added cost minimal, which is precisely what resource-constrained deployment scenarios, from embedded automotive platforms to edge-mounted surveillance cameras, require.

The experimental evaluation is notably broad. The researchers tested DECA-YOLO on five standard pedestrian datasets: WiderPerson, COCO Person, INRIA, Enriched CamPed, and CrowdHuman. These benchmarks collectively span an enormous range of conditions. WiderPerson contains densely packed scenes captured in the wild, CrowdHuman is a benchmark explicitly designed for detecting humans in crowds with heavy occlusion, COCO Person extracts the pedestrian-relevant portions of the widely used Microsoft COCO collection, INRIA is a classic dataset dating back to the era of histogram-of-oriented-gradients detectors, and Enriched CamPed, which the authors previously developed, adds further diversity to the training and evaluation mix. Across all of these datasets, DECA-YOLO demonstrated consistent improvements over the baseline YOLOv7 and over other attention mechanisms, while maintaining real-time inference speed.

The consistency across datasets matters as much as the magnitude of the gains. A module that helps only on one benchmark might be exploiting a dataset-specific quirk, but improvements that hold across dense urban crowds, sparse street scenes, and curated collections suggest that the dual-pooling attention is genuinely learning something generalizable about what makes a pedestrian’s features distinctive. The authors also compared DECA against other attention mechanisms under identical conditions, providing evidence that the benefit comes specifically from the combination of average and max pooling with efficient cross-channel interaction, rather than from the mere presence of any attention module in the backbone.

Transparency and reproducibility are strengths of the work. The authors state that all datasets used in the study are publicly available, and they have released both the code and the trained models on GitHub, allowing other researchers to verify the results, reproduce the experiments, and build on the approach. The research was conducted by Sukesh Babu as part of his PhD studies under the supervision of Dr. Rahul Raman, and the authors declare no conflict of interest and no specific funding for the work. The article was received in August 2025, revised in June 2026, accepted in late August 2026, and published on 15 September 2026 in volume 85 of Multimedia Tools and Applications as article number 760.

The broader significance of DECA-YOLO lies in the ongoing tension between accuracy and efficiency in computer vision. In recent years, the field has seen a proliferation of increasingly large and complex architectures, including vision transformers and detection transformers, some of which now rival or exceed YOLO-style detectors on standard benchmarks. But for pedestrian detection in particular, the deployment environment often cannot accommodate large models. A camera on a bus, a processor inside a car, or a low-power sensor at the edge of a smart city network must run detection continuously, on limited hardware, without lag. Lightweight architectural innovations like DECA, which squeeze more discriminative power out of existing real-time networks rather than demanding bigger ones, are therefore likely to remain highly relevant even as the state of the art advances.

For the autonomous driving industry, the implications are straightforward: better detection of occluded pedestrians directly translates into more reaction time and fewer missed hazards, which is why occlusion-aware detection networks have become a vigorous research area in intelligent transportation systems journals. For surveillance and crowd analytics, the ability to maintain accuracy in dense scenes without sacrificing frame rate opens the door to more reliable deployment in stadiums, transit hubs, and public events. And for the research community, DECA-YOLO offers a clean demonstration that attention mechanisms need not be heavy to be effective, and that revisiting the humble pooling operation, one of the oldest tools in the convolutional toolkit, can still yield measurable gains in one of computer vision’s most consequential applications. With the code and models publicly available, the barrier to testing this claim in practice has never been lower.

Subject of Research: A lightweight dual-pooling channel attention module for real-time pedestrian detection in crowded and occluded scenes

Article Title: DECA-YOLO: Dual-pooling efficient channel attention for real-time pedestrian detection

Article References: S, S. B. V., & Raman, R. (2026). DECA-YOLO: Dual-pooling efficient channel attention for real-time pedestrian detection. Multimedia Tools and Applications, 85(9), Article 760. https://doi.org/10.1007/s11042-026-21903-5

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21903-5

Keywords: pedestrian detection, DECA-YOLO, YOLOv7, channel attention, computer vision, object detection, autonomous driving, deep learning, CrowdHuman, WiderPerson, real-time inference, occlusion

The review, led by Wentao Jiang of Beihang University together with colleagues including Shuicheng Yan of Skywork AI, focuses on four pillars of human-centric vision: person re-identification, human parsing, human pose estimation, and pedestrian detection. All four rely on deep neural networks that can excel on their training data yet stumble on unseen images—a phenomenon known as overfitting. The problem is compounded by the nature of the data itself. Annotating human bodies, with their articulated joints, fine-grained clothing boundaries, and occlusions in crowded scenes, is labor-intensive and costly, while privacy concerns often limit how much real footage of people can be collected and shared in the first place.

To organize a sprawling literature, the authors divide augmentation techniques into two broad families. The first, data perturbation, takes existing images and modifies them to create new training examples. The second, data generation, manufactures entirely new samples, either with graphics engines or with generative models. Within each family, the survey draws a further distinction that turns out to be crucial: some operations act on the whole image, while others target the human figure itself, exploiting knowledge of body structure that generic augmentation methods ignore.

Image-level perturbation is the simplest and most widely used branch. Global transformations such as rotation, scaling, noise injection, and style transfer alter the overall appearance of a picture. Style transfer, which blends the content of one image with the visual style of another, has proven particularly valuable for person re-identification, where images captured by different surveillance cameras exhibit systematic color and lighting differences. By stylizing training images across camera styles, models learn to focus on structural features of a person rather than the color cast of a particular camera. The survey’s experiments on the Market-1501 benchmark, which contains more than 32,000 images of over 1,500 identities filmed by multiple cameras, show that such camera-style augmentation lifts performance well above a baseline SVDNet model, with the strongest results coming from methods that combine generative and discriminative training signals.

Region-level perturbations operate more surgically. Techniques such as random erasing, Cutout, and GridMask deliberately remove or obscure rectangular patches of an image, forcing networks to cope with the partial occlusions that pervade real scenes. A related approach replaces selected regions with grayscale patches, encouraging models to become less dependent on color information, which varies wildly with lighting conditions. The authors caution, however, that these methods walk a fine line: too little occlusion provides no benefit, while excessive or unrealistic erasure can destroy the very features a model needs to learn, degrading performance instead of improving it.

The more distinctive contribution of the survey lies in its treatment of human-level perturbation, where augmentations are guided by the anatomy of the body itself. One technique masks body keypoints with background patches to simulate occluded joints, directly training pose estimation networks to recover from missing evidence. Another uses human parsing to segment a person into semantic parts—head, torso, arms, legs—and then recombines them into a pool from which complex scenes of occlusion and interaction can be synthesized. A third method overlays crops of one person onto another to mimic the crowding typical of pedestrian scenes. At the skeletal level, methods such as PoseTrans apply affine transformations to individual limbs after erasing them from the image, generating a rich variety of plausible poses, while 3D approaches like PoseAug adjust posture, body size, viewpoint, and even split-and-recombine upper and lower body configurations in a differentiable framework that is optimized jointly with the pose estimator. On the MS-COCO benchmark, these human-aware methods, particularly PoseTrans, outperformed generic image-level augmentations when applied to an HRNet-W32 backbone, and on 3D datasets such as Human3.6M and MPI-INF-3DHP, augmentation methods like PoseAug and DH-AUG reduced mean per-joint position error relative to unaugmented baselines.

When perturbation is not enough, researchers turn to outright generation. Graphics-engine approaches render synthetic humans into real backgrounds: MixedPeds, for example, automatically calibrates a virtual camera using the vanishing point of a real dataset, estimates pedestrian scales, and spawns synthetic human agents in unannotated images to train pedestrian detectors. Other systems sample the 3D pose space, deform parametric body models, map on clothing textures, and render the results from varied viewpoints and lighting conditions, producing not only RGB images but also ground-truth 2D and 3D poses, depth maps, surface normals, and body-part segmentation—annotations that would be prohibitively expensive to collect by hand. The survey notes that this is especially important for 3D pose estimation, where ground truth is largely confined to indoor motion-capture studios, causing models trained on such data to generalize poorly to the wild.

Generative models offer a second route to new data. Pose-transfer GANs extract skeletal poses and re-pair them with different appearances, synthesizing images of the same identity in novel poses and clothing—a direct boon for person re-identification, where each identity is typically represented by only a handful of images. Frameworks such as PTGAN and pose-transferring ReID systems add similarity-measurement modules or auxiliary guidance networks to keep generated samples realistic and useful for the downstream task. The survey’s comparison on Market-1501 found that DG-Net, which jointly learns discriminative and generative objectives, achieved the highest mean average precision among the augmentation methods tested. Yet GANs carry well-known liabilities, including training instability and mode collapse, in which the generator produces only a narrow slice of the possible variations.

This is where the survey’s forward-looking analysis becomes most striking. The authors identify pre-trained Latent Diffusion Models, exemplified by Stable Diffusion, as the most promising direction for the field. Diffusion models work by iteratively denoising random noise into coherent images, guided by learned priors, and they sidestep the adversarial discriminator entirely, simplifying training while producing diverse, high-quality samples. The survey sketches concrete applications: controllable diffusion models could generate the same person across outfits, poses, lighting conditions, and camera angles for re-identification; pose-guided synthesis systems such as ControlNet and HyperHuman could manufacture human figures in rare or difficult poses for pose estimation; parsing maps could condition the generation of images with varied clothing and body types for human parsing; and pedestrians could be rendered in diverse urban settings, weather conditions, and crowded or partially obscured scenarios for detection. Beyond full generation, diffusion models could also upgrade perturbation and recombination—seamlessly compositing foreground people into new backgrounds with correct lighting and perspective, swapping one human subject for another while preserving scene realism, and applying subtle changes to clothing or surroundings without the artifacts that plague simple copy-paste and inpainting.

The evidence assembled in the survey suggests that augmentation choices matter as much as architecture choices in human-centric vision. On the CrowdHuman pedestrian detection benchmark, methods such as CrowdAug, SimCP, and SAutoAug consistently improved average precision and Jaccard Index over Faster R-CNN and RetinaNet baselines, and qualitative examples show augmented models detecting pedestrians that baseline systems miss entirely. In human parsing, background replacement and copy-paste techniques sharpen instance segmentation, though the authors note that this subfield still lacks standardized datasets, making comparisons difficult. What emerges overall is a clear principle: methods that respect the structure of the human body—its joints, parts, and the way people occlude one another—consistently outperform generic image tricks. As generative models grow more powerful and controllable, the authors argue, the field stands on the verge of training data that is at once abundant, diverse, and realistic, promising vision systems that see people more robustly in the messy, crowded, and unpredictable environments where they actually operate.

Subject of Research: Data augmentation techniques for human-centric computer vision tasks

Article Title: Data augmentation in human-centric vision

Article References: Jiang, W., Zhang, Y., Zheng, S., Liu, S., & Yan, S. (2024). Data augmentation in human-centric vision. Vicinagearth, 1(1), Article 8. https://doi.org/10.1007/s44336-024-00002-9

Image Credits: AI Generated

DOI: 10.1007/s44336-024-00002-9

Keywords: data augmentation, computer vision, person re-identification, human pose estimation, pedestrian detection, human parsing, generative adversarial networks, diffusion models, synthetic data, deep learning, overfitting, Stable Diffusion