Periodic human activities with implicit workflows are common in manufacturing, sports, and daily life. While short-term periodic activities—characterized by simple structures and high-contrast patterns—have been widely studied, long-term periodic workflows with low-contrast patterns remain largely underexplored. To bridge this gap, we introduce the first benchmark comprising 580 multimodal human activity sequences featuring long-term periodic workflows. The benchmark supports three evaluation tasks aligned with real-world applications: unsupervised periodic workflow detection, task completion tracking, and procedural anomaly detection. We also propose a lightweight, training-free baseline for modeling diverse periodic workflow patterns. Experiments show that: (i) our benchmark presents significant challenges to both unsupervised periodic detection methods and zero-shot approaches based on powerful large language models (LLMs); (ii) our baseline outperforms competing methods by a substantial margin in all evaluation tasks; and (iii) in real-world applications, our baseline demonstrates deployment advantages on par with traditional supervised workflow detection approaches, eliminating the need for annotation and retraining. Our project page is https://sites.google.com/view/periodicworkflow.

Using ceiling-mounted cameras (CMCs) for indoor visual capturing opens up a wide range of applications. However, registering CMCs to the target scene layout presents a challenging task. While manual registration with specialized tools is inefficient and costly, automatic registration with visual localization may yield poor results when visual ambiguity exists. To alleviate these issues, we propose a novel solution for jointly mapping an indoor scene and registering CMCs to the scene layout. Our approach involves equipping a mobile agent with a head-mounted RGB-D camera to traverse the entire scene once and synchronize CMCs to capture this mobile agent. The egocentric videos generate world-coordinate agent trajectories and the scene layout, while the videos of CMCs provide pseudo-scale agent trajectories and CMC relative poses. By correlating all the trajectories with their corresponding timestamps, the CMC relative poses can be aligned to the world-coordinate scene layout. Based on this initialization, a factor graph is customized to enable the joint optimization of ego-camera poses, scene layout, and CMC poses. We also develop a new dataset, setting the first benchmark for collaborative scene mapping and CMC registration. Experimental results indicate that our method not only effectively accomplishes two tasks within a unified framework, but also jointly enhances their performance. We thus provide a reliable tool to facilitate downstream position-aware applications.

We present a robust multi-camera gymnast tracking, which has been applied at international gymnastics championships for gymnastics judging. Despite considerable progress in multi-camera tracking algorithms, tracking gymnasts presents unique challenges: 1) due to space restrictions, only a limited number of cameras can be installed in the gymnastics stadium; and 2) due to variations in lighting, background, uniforms, and occlusions, multi-camera gymnast detection may fail in certain views and only provide valid detections from two opposing views. These factors complicate the accurate determination of a gymnast’s 3D trajectory using conventional multi-camera triangulation. To alleviate this issue, we incorporate gymnastics domain knowledge into our tracking solution. Given that a gymnast’s 3D center typically lies within a predefined vertical plane during much of their performance, we can apply a ray-plane intersection to generate coplanar 3D trajectory candidates for opposing-view detections. More specifically, we propose a novel cascaded data association (DA) paradigm that employs triangulation to generate 3D trajectory candidates when cross-view detections are sufficient, and resort to the ray-plane intersection when they are insufficient. Consequently, coplanar candidates are used to compensate for uncertain trajectories, thereby minimizing tracking failures. The robustness of our method is validated through extensive experimentation, demonstrating its superiority over existing methods in challenging scenarios. Furthermore, our gymnastics judging system, equipped with this tracking method, has been successfully applied to recent Gymnastics World Championships, earning significant recognition from the International Gymnastics Federation.

Action segmentation plays an important role in enabling robots to automatically understand human activities. To train the action recognition model, while obtaining action labels for all frames is costly, annotating timestamp labels for weak supervision is cost-effective. However, existing methods may not fully utilize timestamp labels, which leads to insufficient performance. To alleviate this issue, we proposed a novel learning pattern in our training stage, which maximizes the probability of action union of surrounding timestamps for unlabeled frames. In our inference stage, we provided a new refinement solution to generate better hard-assigned action classes from soft-assigned predictions. Importantly, our methods are model-agnostic and can be applied to existing frameworks. On three commonly used action-segmentation data, our method outperforms previous timestamp-supervision methods and achieves new state-of-the-art performance. More-over, our method uses less than 1% of fully-supervised labels to obtain comparable or even better results.

We propose a Cascaded Buffered IoU (C-BIoU) tracker to track multiple objects that have irregular motions and indistinguishable appearances. When appearance features are unreliable and geometric features are confused by irregular motions, applying conventional Multiple Object Tracking (MOT) methods may generate unsatisfactory results. To address this issue, our C-BIoU tracker adds buffers to expand the matching space of detections and tracks, which mitigates the effect of irregular motions in two aspects: one is to directly match identical but non-overlapping detections and tracks in adjacent frames, and the other is to compensate for the motion estimation bias in the matching space. In addition, to reduce the risk of overexpansion of the matching space, cascaded matching is employed: first matching alive tracks and detections with a small buffer, and then matching unmatched tracks and detections with a large buffer. Despite its simplicity, our C-BIoU tracker works surprisingly well and achieves state-of-the-art results on MOT datasets that focus on irregular motions and indistinguishable appearances. Moreover, the C-BIoU tracker is the dominant component for our 2ndplace solution in the CVPR’22 SoccerNet MOT and the ECCV’22 MOTComplex DanceTrack challenges. Finally, we analyze the limitation of our C-BIoU tracker in ablation studies and discuss its application scope.

Despite significant developments in 3D multi-view multi-person (3D MM) tracking, current frameworks separately target footprint tracking, or pose tracking. Frameworks designed for the former cannot be used for the latter, because they directly obtain 3D positions on the ground plane via a homography projection, which is inapplicable to 3D poses above the ground. In contrast, frameworks designed for pose tracking generally isolate multi-view and multi-frame associations and may not be sufficiently robust for footprint tracking, which utilizes fewer key points than pose tracking, weakening multi-view association cues in a single frame. This study presents a unified multi-view multi-person tracking framework to bridge the gap between footprint tracking and pose tracking. Without additional modifications, the framework can adopt monocular 2D bounding boxes and 2D poses as its input to produce robust 3D trajectories for multiple persons. Importantly, multi-frame and multi-view information are jointly employed to improve association and triangulation. Our framework is shown to provide state-of-the-art performance on the Campus and Shelf datasets for 3D pose tracking, with comparable results on the WILDTRACK and MMPTRACK datasets for 3D footprint tracking.

This paper presents an ultra-low power wireless transceiver specialized for but not limited to medical implantable applications. It operates at the 402-405-MHz medical implant communication service band, and also supports the 420-450-MHz industrial, scientific, and medical band. Being IEEE 802.15.6 standard compliant with additional proprietary modes, this highly configurable transceiver achieves date rates from 11 kb/s to 4.5 Mb/s, which covers the requirements of conventional implantable applications. The phase-locked loop-based transmitter architecture is adopted to support various modulation schemes with limited power budget. The zero-IF receiver has programmable gain and bandwidth to accommodate different operation modes. Fabricated in 40-nm CMOS technology with 1-V supply, this transceiver only consumes 1.78 mW for transmission and 1.49 mW for reception. The ultra-low power consumption together with the 15.6-compliant performance in term of modulation accuracy, sensitivity, and interference robustness make this transceiver competent for various implantable applications.

The wireless communication channel around/in the human body is a difficult propagation environment. This paper presents measurement and simulation results to characterize such a channel. A fluid human body model is employed to emulate the inside of a human body. The paper details the fluid human model and path loss model parameters at 400 MHz (MICS band). It is shown that the simulated and measured results are in a close agreement, for instance at a distance of 20 cm and a implant depth of 10 cm, the measurement results in a path loss of -42.1 dB and the simulation in -43.0 dB. The effect of human model shape on measured path loss is analyzed. Furthermore, simulations are employed to characterize this effect. Using the path loss model a top-level link budget is evaluated to determine the feasibility of a given implant device compliant to IEEE802.15.6-WBAN-400 MHz standard.

Cryptography circuits for smart cards and portable electronic devices provide user authentication and secure data communication. These circuits should, in general, occupy small chip area, consume low power, handle several cryptography algorithms, and provide acceptable performance. This paper presents, for the first time, a hardware implementation of three standard cryptography algorithms on a universal architecture. The microcoded cryptography processor targets smart card applications and implements both private key and public key algorithms and meets the power and performance specifications and is as small as 2.25 mm/sup 2/ in 0.18-/spl mu/m 6LM CMOS. A new algorithm is implemented by changing the contents of the memory blocks that are implemented in ferroelectric RAM (FeRAM). Using FeRAM allows nonvolatile storage of the configuration bits, which are changed only when a new algorithm instantiation is required.