Maps of dynamics are effective representations of motion patterns learned from prior observations, with recent research demonstrating their ability to enhance performance in various downstream tasks such as human-aware robot navigation, long-term human motion prediction, and robot localization. Current advancements have primarily concentrated on methods for learning maps of human flow in environments where the flow is static, i.e., not assumed to change over time. In this paper we propose a method to update the CLiFF-map, one type of map of dynamics, for achieving efficient life-long robot operation. As new observations are collected, our goal is to update a CLiFF-map to effectively and accurately integrate new observations, while retaining relevant historic motion patterns. The proposed online update method maintains a probabilistic representation in each observed location, updating parameters by continuously tracking sufficient statistics. In experiments using both synthetic and real-world datasets, we show that our method is able to maintain accurate representations of human motion dynamics, contributing to high performance flow-compliant planning downstream tasks, while being orders of magnitude faster than the comparable baselines.

Generating realistic human flows across regions is essential for our understanding of urban structures and population activity patterns, enabling important applications in the fields of urban planning and management. However, a notable shortcoming of most existing mobility generation methodologies is neglect of prediction fairness, which can result in underestimation of mobility flows across regions with vulnerable population groups, potentially resulting in inequitable resource distribution and infrastructure development. To overcome this limitation, our study presents a novel, fairness-aware deep learning model, FairMobi-Net, for inter-region human flow prediction. The FairMobi-Net model uniquely incorporates fairness loss into the loss function and employs a hybrid approach, merging binary classification and numerical regression techniques for human flow prediction. We validate the FairMobi-Net model using comprehensive human mobility datasets from four U.S. cities, predicting human flow at the census-tract level. Our findings reveal that the FairMobi-Net model outperforms state-of-the-art models (such as the DeepGravity model) in producing more accurate and equitable human flow predictions across a variety of region pairs, regardless of regional income differences. The model maintains a high degree of accuracy consistently across diverse regions, addressing the previous fairness concern. Further analysis of feature importance elucidates the impact of physical distances and road network structures on human flows across regions. With fairness as its touchstone, the model and results provide researchers and practitioners across the fields of urban sciences, transportation engineering, and computing with an effective tool for accurate generation of human mobility flows across regions.

Multi-view approaches to people-tracking have the potential to better handle occlusions than single-view ones in crowded scenes. They often rely on the tracking-by-detection paradigm, which involves detecting people first and then connecting the detections. In this paper, we argue that an even more effective approach is to predict people motion over time and infer people's presence in individual frames from these. This enables to enforce consistency both over time and across views of a single temporal frame. We validate our approach on the PETS2009 and WILDTRACK datasets and demonstrate that it outperforms state-of-the-art methods.

A multiple-exposure portrait of Chinese contemporary artist and human rights activist Ai Weiwei, made on film in Beverly Hills, on the occasion of his new documentary, "Human Flow." A multiple-exposure portrait of Chinese contemporary artist and human rights activist Ai Weiwei, made on film in Beverly Hills, on the occasion of his new documentary, "Human Flow." (Jay L. Clendenin / Los Angeles Times) Ai Weiwei is nothing if not prolific. He spent the better part of 2016 traveling around the globe visiting refugee camps for his new documentary feature film, "Human Flow," debuting in theaters this month. He's made so much art that he currently has work in 12 museum and gallery exhibitions around the world -- eight of them solo shows. In New York, the contemporary artist and social justice activist is installing some 300 works across the city's five boroughs for the Public Art Fund exhibition "Good Fences Make Good Neighbors," opening Oct. 12.

Ai Weiwei may be China's most famous contemporary artist and a prolific social justice activist. But at his core, Ai insists, he is simply an observer. Not to mention a relentless documenter -- of the Chinese communist government, of international human rights violations, of the 40-some cats that roam his Beijing art studio and of the longtime team members who populate his Berlin art studio, a 150-year-old underground beer cellar. Tonight it's the moon that has captured Ai's attention. He arrived a few hours ago at LAX and now strolls languidly across his agent's Beverly Hills office courtyard, repeatedly stopping to take photos of the sky.