Decomposed Human Motion Prior for Video Pose Estimation via Adversarial Training

Estimating human pose from video is a task that receives considerable attention due to its applicability in numerous 3D fields. The complexity of prior knowledge of human body movements poses a challenge to neural network models in the task of regressing keypoints. In this paper, we address this problem by incorporating motion prior in an adversarial way. Different from previous methods, we propose to decompose holistic motion prior to joint motion prior, making it easier for neural networks to learn from prior knowledge thereby boosting the performance on the task. We also utilize a novel regularization loss to balance accuracy and smoothness introduced by motion prior. Our method achieves 9\% lower PA-MPJPE and 29\% lower acceleration error than previous methods tested on 3DPW. The estimator proves its robustness by achieving impressive performance on in-the-wild dataset

Modeling and Recognizing Driver Behavior Based on Driving Data: A Survey

In recent years, modeling and recognizing driver behavior have become crucial to understanding intelligence transport systems, human-vehicle systems, and intelligent vehicle systems. A wide range of both mathematical identification methods and modeling methods of driver behavior are presented from the control point of view in this paper based on the driving data, such as the brake/throttle pedal position and the steering wheel angle, among others. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems, and the evaluation and verification of vehicle systems based on the driver model are described

Shareable Driving Style Learning and Analysis with a Hierarchical Latent Model

Driving style is usually used to characterize driving behavior for a driver or a group of drivers. However, it remains unclear how one individual's driving style shares certain common grounds with other drivers. Our insight is that driving behavior is a sequence of responses to the weighted mixture of latent driving styles that are shareable within and between individuals. To this end, this paper develops a hierarchical latent model to learn the relationship between driving behavior and driving styles. We first propose a fragment-based approach to represent complex sequential driving behavior, allowing for sufficiently representing driving behavior in a low-dimension feature space. Then, we provide an analytical formulation for the interaction of driving behavior and shareable driving style with a hierarchical latent model by introducing the mechanism of Dirichlet allocation. Our developed model is finally validated and verified with 100 drivers in naturalistic driving settings with urban and highways. Experimental results reveal that individuals share driving styles within and between them. We also analyzed the influence of personalities (e.g., age, gender, and driving experience) on driving styles and found that a naturally aggressive driver would not always keep driving aggressively (i.e., could behave calmly sometimes) but with a higher proportion of aggressiveness than other types of drivers

Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising

Leveraging large-scale image-text datasets and advancements in diffusion models, text-driven generative models have made remarkable strides in the field of image generation and editing. This study explores the potential of extending the text-driven ability to the generation and editing of multi-text conditioned long videos. Current methodologies for video generation and editing, while innovative, are often confined to extremely short videos (typically less than 24 frames) and are limited to a single text condition. These constraints significantly limit their applications given that real-world videos usually consist of multiple segments, each bearing different semantic information. To address this challenge, we introduce a novel paradigm dubbed as Gen-L-Video, capable of extending off-the-shelf short video diffusion models for generating and editing videos comprising hundreds of frames with diverse semantic segments without introducing additional training, all while preserving content consistency. We have implemented three mainstream text-driven video generation and editing methodologies and extended them to accommodate longer videos imbued with a variety of semantic segments with our proposed paradigm. Our experimental outcomes reveal that our approach significantly broadens the generative and editing capabilities of video diffusion models, offering new possibilities for future research and applications. The code is available at https://github.com/G-U-N/Gen-L-Video.Comment: The code is available at https://github.com/G-U-N/Gen-L-Vide

Real-Time Scalable Visual Tracking via Quadrangle Kernelized Correlation Filters

Correlation filter (CF) has been widely used in tracking tasks due to its simplicity and high efficiency. However, conventional CF-based trackers fail to handle the scale variation that occurs when the targeted object is moving, which is one of the most notable unsolved problems of visual object tracking. In this paper, we propose a scalable visual tracking algorithm based on kernelized correlation filters, referred to as quadrangle kernelized correlation filters (QKCF). Unlike existing complicated scalable trackers that either perform the correlation filtering operation multiple times or extract many candidate windows at various scales, our tracker intends to estimate the scale of the object based on the positions of its four corners, which can be detected using a new Gaussian training output matrix within one filtering process. After obtaining four peak values corresponding to the four corners, we measure the detection confidence of each part response by evaluating its spatial and temporal smoothness. On top of it, a weighted Bayesian inference framework is employed to estimate the final location and size of the bounding box from the response matrix, where the weights are synchronized with the calculated detection likelihoods. Experiments are performed on the OTB-100 data set and 16 benchmark sequences with significant scale variations. The results demonstrate the superiority of the proposed method in terms of both effectiveness and robustness, compared with the state-of-the-art methods

A New Type of Quartz Smog Chamber : Design and Characterization

Publisher Copyright: ©Since the 1960s, many indoor and outdoor smog chambers have been developed worldwide. However, most of them are made of Teflon films, which have relatively high background contaminations due to the wall effect. We developed the world's first medium-size quartz chamber (10 m(3)), which is jointed with 32 pieces of 5 mm thick polished quartz glasses and a stainless-steel frame. Characterizations show that this chamber exhibits excellent performance in terms of relative humidity (RH) (2-80%) and temperature (15-30 +/- 1 degrees C) control, mixing efficiency of the reactants (6-8 min), light transmittance (>90% above 290 nm), and wall loss of pollutants. The wall loss rates of the gas-phase pollutants are on the order of 10(-4) min(-1) at 298 K under dry conditions. It is 0.08 h(-1) for 100-500 nm particles, significantly lower than those of Teflon chambers. The photolysis rate of NO2 (J(NO2)) is automatically adjustable to simulate the diurnal variation of solar irradiation from 0 to 0.40 min(-1). The inner surface of the chamber can be repeatedly washed with deionized water, resulting in low background contaminations. Both experiments (toluene-NOx and alpha-pinene-ozone systems) and box model demonstrate that this new quartz chamber can provide high-quality data for investigating SOA and O-3 formation in the atmosphere.Peer reviewe

Long-term outcomes of radiofrequency ablation vs. partial nephrectomy for cT1 renal cancer: A meta-analysis and systematic review

BackgroundPartial nephrectomy (PN) is one of the most preferred nephron-sparing treatments for clinical T1 (cT1) renal cancer, while radiofrequency ablation (RFA) is usually used for patients who are poor surgical candidates. The long-term oncologic outcome of RFA vs. PN for cT1 renal cancer remains undetermined. This meta-analysis aims to compare the treatment efficacy and safety of RFA and PN for patients with cT1 renal cancer with long-term follow-up of at least 5 years.MethodThis meta-analysis was performed following the PRISMA reporting guidelines. Literature studies that had data on the comparison of the efficacy or safety of RFA vs. PN in treating cT1 renal cancer were searched in databases including PubMed, Embase, Web of Science, and the Cochrane Library from 1 January2000 to 1 May 2022. Only long-term studies with a median or mean follow-up of at least 5 years were included. The following measures of effect were pooled: odds ratio (OR) for recurrence and major complications; hazard ratio (HR) for progression-free survival (PFS), cancer-specific survival (CSS), and overall survival (OS). Additional analyses, including sensitivity analysis, subgroup analysis, and publication bias analysis, were also performed.ResultsA total of seven studies with 1,635 patients were finally included. The treatment efficacy of RFA was not different with PN in terms of cancer recurrence (OR = 1.22, 95% CI, 0.45–3.28), PFS (HR = 1.26, 95% CI, 0.75–2.11), and CSS (HR = 1.27, 95% CI, 0.41–3.95) as well as major complications (OR = 1.31, 95% CI, 0.55–3.14) (P > 0.05 for all). RFA was a potential significant risk factor for OS (HR = 1.76, 95% CI, 1.32–2.34, P < 0.001). No significant heterogeneity and publication bias were observed.ConclusionThis is the first meta-analysis that focuses on the long-term oncological outcomes of cT1 renal cancer, and the results suggest that RFA has comparable therapeutic efficacy with PN. RFA is a nephron-sparing technique with favorable oncologic efficacy and safety and a good treatment alternative for cT1 renal cancer