Crowd surveillance plays a key role to ensure safety and security in public areas. Surveillance systems traditionally rely on fixed camera networks, which suffer from limitations, as coverage of the monitored area, video resolution and analytic performance. On the other hand, a smart camera network provides the ability to reconfigure the sensing infrastructure by incorporating active devices such as pan-tilt-zoom (PTZ) cameras and UAV-based cameras, thus enabling the network to adapt over time to changes in the scene. We propose a new decentralised approach for network reconfiguration, where each camera dynamically adapts its parameters and position to optimise scene coverage. Two policies for decentralised camera reconfiguration are presented: a greedy approach and a reinforcement learning approach. In both cases, cameras are able to locally control the state of their neighbourhood and dynamically adjust their position and PTZ parameters. When crowds are present, the network balances between global coverage of the entire scene and high resolution for the crowded areas. We evaluate our approach in a simulated environment monitored with fixed, PTZ and UAV-based cameras.

Motion retargeting aims at transferring a given motion from a source character to a target one. The task becomes increasingly challenging as the differences between the body shape and skeletal structure of input and target characters increase. We present a novel approach for motion retargeting between skeletons whose goal is to transfer the motion from a source skeleton to a target one in a different format. Our approach works when the two skeletons differ in scale, bone length, and number of joints. Surpassing previous approaches, our method can also retarget between skeletons that differ in hierarchy and topology, such as retargeting between animals and humans. We train our method as a transformer using a random masking strategy both in time and space, aiming at reconstructing the joints of the masked input skeleton to obtain a deep representation of the motion. At testing time, our proposal can retarget the input motion to different skeletons, reconstructing the disparities between the source and the target. Our method outperforms state-of-the-art results on the Mixamo dataset, which features a high variance between skeleton formats. Moreover, we show how our approach can effectively generalize to different domains by transferring between human motion and quadrupeds, and vice-versa. Our code is available at www.github.com/mmlab-cv/skeleton-aware-motion-retargeting.

Simulation is a powerful tool to easily generate annotated data, and a highly desirable feature, especially in those domains where learning models need large training datasets. Machine learning and deep learning solutions, have proven to be extremely data-hungry and sometimes, the available real-world data are not sufficient to effectively model the given task. Despite the initial skepticism of a portion of the scientific community, the potential of simulation has been largely confirmed in many application areas, and the recent developments in terms of rendering and virtualization engines, have shown a good ability also in representing complex scenes. This includes environmental factors, such as weather conditions and surface reflectance, as well as human-related events, like human actions and behaviors. We present a human crowd simulator, called UniCrowd, and its associated validation pipeline. We show how the simulator can generate annotated data, suitable for computer vision tasks, in particular for detection and segmentation, as well as the related applications, as crowd counting, human pose estimation, trajectory analysis and prediction, and anomaly detection.