pwc-archive/papers-with-abstracts huggingface dataset:
Act Now: A Novel Online Forecasting Framework for Large-Scale Streaming Data
Authors: Daojun Liang, Haixia Zhang, Jing Wang, Dongfeng Yuan, Minggao Zhang
Date: 2024-11-28 00:00:00
Tasks: GPU

In this paper, we find that existing online forecasting methods have the following issues: 1) They do not consider the update frequency of streaming data and directly use labels (future signals) to update the model, leading to information leakage. 2) Eliminating information leakage can exacerbate concept drift and online parameter updates can damage prediction accuracy. 3) Leaving out a validation set cuts off the model's continued learning. 4) Existing GPU devices cannot support online learning of large-scale streaming data. To address the above issues, we propose a novel online learning framework, Act-Now, to improve the online prediction on large-scale streaming data. Firstly, we introduce a Random Subgraph Sampling (RSS) algorithm designed to enable efficient model training. Then, we design a Fast Stream Buffer (FSB) and a Slow Stream Buffer (SSB) to update the model online. FSB updates the model immediately with the consistent pseudo- and partial labels to avoid information leakage. SSB updates the model in parallel using complete labels from earlier times. Further, to address concept drift, we propose a Label Decomposition model (Lade) with statistical and normalization flows. Lade forecasts both the statistical variations and the normalized future values of the data, integrating them through a combiner to produce the final predictions. Finally, we propose to perform online updates on the validation set to ensure the consistency of model learning on streaming data. Extensive experiments demonstrate that the proposed Act-Now framework performs well on large-scale streaming data, with an average 28.4% and 19.5% performance improvement, respectively. Experiments can be reproduced via https://github.com/Anoise/Act-Now.
Efficient online multi-camera tracking with memory-efficient accumulated appearance features and trajectory validation
Authors: Lap Quoc Tran, Huan Duc Vi
Date: 2024-06-17 00:00:00
Proceeding: CVPR 2024 6
Tasks: Multi-Object Tracking

Multi-camera tracking (MCT) plays a crucial role in various computer vision applications. However, accurate tracking of individuals across multiple cameras faces challenges, particularly with identity switches. In this paper, we present an efficient online MCT system that tackles these challenges through online processing. Our system leverages memory-efficient accumulated appearance features to provide stable representations of individuals across cameras and time. By incorporating trajectory validation using hierarchical agglomerative clustering (HAC) in overlapping regions, ID transfers are identified and rectified. Evaluation on the 2024 AI City Challenge Track 1 dataset [39] demonstrates the competitive performance of our system, achieving accurate tracking in both overlapping and nonoverlapping camera networks. With a 40.3% HOTA score [29], our system ranked 9th in the challenge. The integration of trajectory validation enhances performance by 8% over the baseline, and the accumulated appearance features further contribute to a 17% improvement.
Online Multi-camera People Tracking with Spatial-temporal Mechanism and Anchor-feature Hierarchical Clustering
Authors: Riu Cherdchusakulchai, Sasin Phimsiri, Visarut Trairattanapa, Suchat Tungjitnob, Wasu Kudisthalert, Pornprom Kiawjak, Ek Thamwiwatthana, Phawat Borisuitsawat, Teepakorn Tosawadi, Pakcheera Choppradit, Kasisdis Mahakijdechachai, Supawit Vatathanavaro, Worawit Saetan, Vasin Suttichaya
Date: 2024-06-17 00:00:00
Proceeding: CVPR 2024 6
Tasks: Multi-Object Tracking, Object, Object Tracking

Multi-camera Multi-object tracking (MTMC) surpasses conventional single-camera tracking by enabling seamless object tracking across multiple camera views. This capability is critical for security systems and improving situational awareness in various environments. This paper proposes a novel MTMC framework designed for online operation. The framework employs a three-stage pipeline: Multi-object Tracking (MOT) Multi-target Multi-camera Tracking (MTMC) and Cross Interval Synchronization (CIS). In the MOT stage ReID features are extracted and localized tracklets are created. MTMC links these tracklets across cameras using spatial-temporal constraints and constraint hierarchical clustering with anchor features for improved inter-camera association. Finally CIS ensures the temporal coherence of tracklets across time intervals. The proposed framework achieves robust tracking performance validated on the challenging 2024 AI City Challenge with a HOTA score of 51.0556% ranking sixth. The code is available at: https://github.com/AI-and-Robotics-Ventures/AIC2024_Track1_ARV
An Online Approach and Evaluation Method for Tracking People Across Cameras in Extremely Long Video Sequence
Authors: Cheng-Yen Yang, Hsiang-Wei Huang, Pyong-Kun Kim, Zhongyu Jiang, Kwang-Ju Kim, Chung-I Huang, Haiqing Du, Jenq-Neng Hwang
Date: 2024-06-17 00:00:00
Proceeding: CVPR 2024 6
Tasks: Camera Calibration, Domain Generalization, Multi-Object Tracking, Object Tracking

Multi-camera Multi-Object Tracking has drawn significant attention in recent years due to its critical role in surveillance analytics and related fields. Various challenges including non-overlapping regions varying occlusion conditions and the need for cross-domain generalization in multi-camera tracking systems remain unsolved in the field. We propose a novel online tracking framework that capitalizes on real-time camera calibration to achieve consistent multi-object tracking across camera networks. Our approach seamlessly integrates spatial and temporal association techniques ensuring robust tracking even in long-duration videos. However standard tracking evaluation metrics like CLEAR or HOTA fall short of accurately interpreting the performance of tracking over extended video sequences. Another contribution of this study is the proposal of a new evaluation metric mHOTA which provides a better assessment of tracking performance over prolonged periods. Our comprehensive experiments on the AIC24 Multi-Camera People Tracking dataset demonstrate the effectiveness and scalability of our method along with the capability of the proposed evaluation metric.
OCMCTrack: Online Multi-Target Multi-Camera Tracking with Corrective Matching Cascade
Authors: Andreas Specker
Date: 2024-06-17 00:00:00
Proceeding: CVPR 2024 6
Tasks: Multi-Object Tracking, Position

The implementation of multi-target multi-camera tracking systems in indoor environments including shops and warehouses facilitates strategic product positioning and the improvement of operational workflows. This paper presents the online multi-target multi-camera tracking framework OCMCTrack which tracks the 3D positions of people in the world. The proposed framework introduces a novel matching cascade to re-evaluate track assignments dynamically thus minimizing false positive associations often made by online trackers. Additionally this work presents three effective methods to enhance the transformation of a person's position in the image to world coordinates thereby addressing common inaccuracies in positional reference points. The proposed methodology is able to achieve competitive performance in Track 1 of the 2024 AI City Challenge demonstrating the effectiveness of the framework.
Cluster Self-Refinement for Enhanced Online Multi-Camera People Tracking
Authors: Jeongho Kim, Wooksu Shin, Hancheol Park, Donghyuk Choi
Date: 2024-06-17 00:00:00
Proceeding: CVPR 2024 6
Tasks: Multi-Object Tracking

Recently there has been a significant amount of research on Multi-Camera People Tracking (MCPT). MCPT presents more challenges compared to Multi-Object Single Camera Tracking leading many existing studies to address them using offline methods. However offline methods can only analyze pre-recorded videos which presents less practical application in real industries compared to online methods. Therefore we aimed to focus on resolving major problems that arise when using the online approach. Specifically to address problems that could critically affect the per- formance of the online MCPT such as storing inaccurate or low-quality appearance features and situations where a person is assigned multiple IDs we proposed a Cluster Self- Refinement module. We achieved a third-place at the 2024 AI City Challenge Track 1 with a HOTA score of 60.9261% and our code is available at https://github.com/ nota-github/AIC2024_Track1_Nota.
A Robust Online Multi-Camera People Tracking System With Geometric Consistency and State-aware Re-ID Correction
Authors: Zhenyu Xie, Zelin Ni, Wenjie Yang, Yuang Zhang, Yihang Chen, Yang Zhang, Xiao Ma
Date: 2024-06-17 00:00:00
Proceeding: CVPR 2024 6
Tasks: Multi-Object Tracking, Multiple People Tracking

Multi-camera multiple people tracking is a crucial technology for surveillance crowd management and social behavior analysis enabling large-scale monitoring and comprehensive understanding of complex scenarios involving multiple individuals across different camera views. However due to severe occlusion within the scene and significant variations in camera viewpoints there are high demands for matching and correlating the same target among different cameras especially in an online setting. To address this challenge we propose a novel online multi-camera multiple people tracking system. This system integrates geometric-consistent constraints and appearance information of the targets effectively improving tracking accuracy. Additionally we design a state-aware Re-ID correction mechanism that adaptively leverages Re-ID features to correct mismatches among targets. This system has demonstrated good adaptability across various scenarios. Our proposed system is evaluated in track1 of the 2024 AI City Challenge achieving a HOTA score of 67.2175% and securing the 2nd position on the leaderboard. The code will be available at: https://github.com/ZhenyuX1E/PoseTrack
Overlap Suppression Clustering for Offline Multi-Camera People Tracking
Authors: Ryuto Yoshida, Junichi Okubo, Junichiro Fujii, Masazumi Amakata, Takayoshi Yamashita
Date: 2024-06-17 00:00:00
Proceeding: CVPR 2024 6
Tasks: Clustering, Multi-Object Tracking, Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking, Person Re-Identification, Pose Estimation

Multi-Camera People Tracking is a multifaceted issue that requires the integration of several computer vision tasks such as Object Detection Multiple Object Tracking and Person Re-identification. This study presents a multi-camera people tracking method that comprises four main processes: (1) single camera people tracking based on overlap suppression clustering (2) representative image extraction using pose estimation for re-identification (3) re-identification using hierarchical clustering with average linkage and (4) low-identifiability tracklets assignment. Our RIIPS team achieved the highest Higher Order Tracking Accuracy (HOTA) of 71.9446% in the 2024 AI City Challenge Track 1.
JoyGen: Audio-Driven 3D Depth-Aware Talking-Face Video Editing
Authors: Qili Wang, Dajiang Wu, Zihang Xu, Junshi Huang, Jun Lv
Date: 2025-01-03 00:00:00
Tasks: 3D Reconstruction, Face Generation, Motion Generation, Talking Face Generation, Video Editing, Video Generation

Significant progress has been made in talking-face video generation research; however, precise lip-audio synchronization and high visual quality remain challenging in editing lip shapes based on input audio. This paper introduces JoyGen, a novel two-stage framework for talking-face generation, comprising audio-driven lip motion generation and visual appearance synthesis. In the first stage, a 3D reconstruction model and an audio2motion model predict identity and expression coefficients respectively. Next, by integrating audio features with a facial depth map, we provide comprehensive supervision for precise lip-audio synchronization in facial generation. Additionally, we constructed a Chinese talking-face dataset containing 130 hours of high-quality video. JoyGen is trained on the open-source HDTF dataset and our curated dataset. Experimental results demonstrate superior lip-audio synchronization and visual quality achieved by our method.
SwinTExCo: Exemplar-based video colorization using Swin Transformer
Authors: Duong Thanh Tran, Nguyen Doan Hieu Nguyen, Trung Thanh Pham, Phuong-Nam Tran, Thuy-Duong Thi Vu, Cuong Tuan Nguyen, Hanh Dang-Ngoc, Duc Ngoc Minh Dang
Date: 2025-01-15 00:00:00
Proceeding: Expert Systems with Applications 2025 1
Tasks: Colorization, Video Restoration

Video colorization represents a compelling domain within the field of Computer Vision. The traditional approach in this field relies on Convolutional Neural Networks (CNNs) to extract features from each video frame and employs a recurrent network to learn information between video frames. While demonstrating considerable success in colorization, most traditional CNNs suffer from a limited receptive field size, capturing local information within a fixed-sized window. Consequently, they struggle to directly grasp long-range dependencies or pixel relationships that span large image or video frame areas. To address this limitation, recent advancements in the field have leveraged Vision Transformer (ViT) and their variants to enhance performance. This article introduces Swin Transformer Exemplar-based Video Colorization (SwinTExCo), an end-to-end model for the video colorization process that incorporates the Swin Transformer architecture as the backbone. The experimental results demonstrate that our proposed method outperforms many other state-of-the-art methods in both quantitative and qualitative metrics. The achievements of this research have significant implications for the domain of documentary and history video restoration, contributing to the broader goal of preserving cultural heritage and facilitating a deeper understanding of historical events through enhanced audiovisual materials.
Merging Context Clustering with Visual State Space Models for Medical Image Segmentation
Authors: Yun Zhu, Dong Zhang, Yi Lin, Yifei Feng, Jinhui Tang
Date: 2025-01-03 00:00:00
Tasks: Clustering, Image Segmentation, Mamba, Medical Image Segmentation, Semantic Segmentation, State Space Models

Medical image segmentation demands the aggregation of global and local feature representations, posing a challenge for current methodologies in handling both long-range and short-range feature interactions. Recently, vision mamba (ViM) models have emerged as promising solutions for addressing model complexities by excelling in long-range feature iterations with linear complexity. However, existing ViM approaches overlook the importance of preserving short-range local dependencies by directly flattening spatial tokens and are constrained by fixed scanning patterns that limit the capture of dynamic spatial context information. To address these challenges, we introduce a simple yet effective method named context clustering ViM (CCViM), which incorporates a context clustering module within the existing ViM models to segment image tokens into distinct windows for adaptable local clustering. Our method effectively combines long-range and short-range feature interactions, thereby enhancing spatial contextual representations for medical image segmentation tasks. Extensive experimental evaluations on diverse public datasets, i.e., Kumar, CPM17, ISIC17, ISIC18, and Synapse demonstrate the superior performance of our method compared to current state-of-the-art methods. Our code can be found at https://github.com/zymissy/CCViM.
12-bit Delta-Sigma ADC operating at a temperature of up to 250C in Standard 0.18 $μ$m SOI CMOS
Authors: Christian Sbrana, Alessandro Catania, Tommaso Toschi, Sebastiano Strangio, Giuseppe Iannaccone
Date: 2024-12-31 00:00:00

Some applications require electronic systems to operate at extremely high temperature. Extending the operating temperature range of automotive-grade CMOS processes -- through the use of dedicated design techniques -- can provide an important cost-effective advantage. We present a second-order discrete-time delta-sigma analog-to-digital converter operating at a temperature of up to 250 $^\circ$C, well beyond the 175 $^\circ$C qualification temperature of the automotive-grade CMOS process used for its fabrication (XFAB XT018). The analog-to-digital converter incorporates design techniques that are effective in mitigating the adverse effects of the high temperature, such as increased leakage currents and electromigration. We use configurations of dummy transistors for leakage compensation, clock-boosting methods to limit pass-gate cross-talk, and we optimized the circuit architecture to ensure stability and accuracy at high temperature. Comprehensive measurements demonstrate that the analog-to-digital converter achieves a signal-to-noise ratio exceeding 93 dB at 250 $^\circ$C, with an effective number of bits of 12, and a power consumption of only 44~mW. The die area of the converter is only 0.065~mm$^2$ and the area overhead of the high-temperature mitigation circuits is only 13.7%. The Schreier Figure of Merit is 140~dB at the maximum temperature of 250 $^\circ$C, proving the potential of the circuit for reliable operation in challenging applications such as gas and oil extraction and aeronautics.
Can Human Drivers and Connected Autonomous Vehicles Co-exist in Lane-Free Traffic? A Microscopic Simulation Perspective
Authors: Arslan Ali Syed, Majid Rostami-Shahrbabaki, Klaus Bogenberger
Date: 2025-01-02 00:00:00
Tasks: Autonomous Vehicles

Recent advancements in connected autonomous vehicle (CAV) technology have sparked growing research interest in lane-free traffic (LFT). LFT envisions a scenario where all vehicles are CAVs, coordinating their movements without lanes to achieve smoother traffic flow and higher road capacity. This potentially reduces congestion without building new infrastructure. However, the transition phase will likely involve non-connected actors such as human-driven vehicles (HDVs) or independent AVs sharing the roads. This raises the question of how LFT performance is impacted when not all vehicles are CAVs, as these non-connected vehicles may prioritize their own benefits over system-wide improvements. This paper addresses this question through microscopic simulation on a ring road, where CAVs follow the potential lines (PL) controller for LFT, while HDVs adhere to a strip-based car-following model. The PL controller is also modified for safe velocities to prevent collisions. The results reveal that even a small percentage of HDVs can significantly disrupt LFT flow: 5% HDVs can reduce LFT's maximum road capacity by 16%, and a 20% HDVs nearly halves it. The study also develops an adaptive potential (APL) controller that forms APL corridors with modified PLs in the surroundings of HDVs. APL shows a peak traffic flow improvement of 23.6% over the PL controller. The study indicates that a penetration rate of approximately 60% CAVs in LFT is required before significant benefits of LFT start appearing compared to a scenario with all HDVs. These findings open a new research direction on minimizing the adverse effects of non-connected vehicles on LFT.
Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep RL Approach
Authors: Noor Aboueleneen, Yahuza Bello, Abdullatif Albaseer, Ahmed Refaey Hussein, Mohamed Abdallah, Ekram Hossain
Date: 2024-12-31 00:00:00
Tasks: Autonomous Vehicles, Management, Multi-agent Reinforcement Learning, Navigate

Autonomous Vehicles (AVs) represent a transformative advancement in the transportation industry. These vehicles have sophisticated sensors, advanced algorithms, and powerful computing systems that allow them to navigate and operate without direct human intervention. However, AVs' systems still get overwhelmed when they encounter a complex dynamic change in the environment resulting from an accident or a roadblock for maintenance. The advanced features of Sixth Generation (6G) technology are set to offer strong support to AVs, enabling real-time data exchange and management of complex driving maneuvers. This paper proposes a Multi-Agent Reinforcement Learning (MARL) framework to improve AVs' decision-making in dynamic and complex Intelligent Transportation Systems (ITS) utilizing 6G-V2X communication. The primary objective is to enable AVs to avoid roadblocks efficiently by changing lanes while maintaining optimal traffic flow and maximizing the mean harmonic speed. To ensure realistic operations, key constraints such as minimum vehicle speed, roadblock count, and lane change frequency are integrated. We train and test the proposed MARL model with two traffic simulation scenarios using the SUMO and TraCI interface. Through extensive simulations, we demonstrate that the proposed model adapts to various traffic conditions and achieves efficient and robust traffic flow management. The trained model effectively navigates dynamic roadblocks, promoting improved traffic efficiency in AV operations with more than 70% efficiency over other benchmark solutions.
Privacy Preservation in MIMO-OFDM Localization Systems: A Beamforming Approach
Authors: Yuchen Zhang, Hui Chen, Musa Furkan Keskin, Alireza Pourafzal, Pinjun Zheng, Henk Wymeersch, Tareq Y. Al-Naffouri
Date: 2025-01-02 00:00:00

We investigate an uplink MIMO-OFDM localization scenario where a legitimate base station (BS) aims to localize a user equipment (UE) using pilot signals transmitted by the UE, while an unauthorized BS attempts to localize the UE by eavesdropping on these pilots, posing a risk to the UE's location privacy. To enhance legitimate localization performance while protecting the UE's privacy, we formulate an optimization problem regarding the beamformers at the UE, aiming to minimize the Cram\'er-Rao bound (CRB) for legitimate localization while constraining the CRB for unauthorized localization above a threshold. A penalty dual decomposition optimization framework is employed to solve the problem, leading to a novel beamforming approach for location privacy preservation. Numerical results confirm the effectiveness of the proposed approach and demonstrate its superiority over existing benchmarks.
The Limits of Tolerance
Authors: Alan D. Miller
Date: 2024-12-31 00:00:00

I propose a model of aggregation of intervals relevant to the study of legal standards of tolerance. Seven axioms: responsiveness, anonymity, continuity, strategyproofness, and three variants of neutrality are then used to prove several important results about a new class of aggregation methods called endpoint rules. The class of endpoint rules includes extreme tolerance (allowing anything permitted by anyone) and a form of majoritarianism (the median rule).